{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "04f8dd9e-724a-4d80-8713-b9509574f2df",
   "metadata": {},
   "source": [
    "## Code for Analyazing User Activity on Reddit\n",
    "\n",
    "*The following notebook contains code that was used in the analysis of user activity on select TV subreddits.* "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "496de650-632d-4105-8a62-2f387f498b6c",
   "metadata": {},
   "source": [
    "### Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b6859283",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "from collections import Counter\n",
    "import json\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.cluster import hierarchy\n",
    "from scipy.spatial.distance import squareform\n",
    "from matplotlib.colors import Normalize\n",
    "from matplotlib.cm import ScalarMappable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80dd7e34-7982-432f-9ff9-404e773bb01b",
   "metadata": {},
   "source": [
    "### Load Data (for global super hits only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f4f307e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1616889, 7)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import user comment data by subreddit\n",
    "df1 = pd.read_csv('Data/User_Comment_Counts_1.csv', encoding='utf-8')\n",
    "df2 = pd.read_csv('Data/User_Comment_Counts_2.csv', encoding='utf-8')\n",
    "\n",
    "df = pd.concat([df1,df2], ignore_index=True)\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19440f88",
   "metadata": {},
   "outputs": [],
   "source": [
    "#standardize the show labels\n",
    "label_mapping = {\n",
    "    '\\'The Mandalorian\\'': 'Mandalorian',\n",
    "    '\\'One Piece\\'': 'One Piece',\n",
    "    '\\'La Casa De Papel\\'': 'Money Heist',\n",
    "    '\\'Vikings\\'': 'Vikings',\n",
    "    '\\\\\\'[\\\"The Handmaid\\\\\\\\\\\\\\'s Tale\\\"]\\\\\\']': 'Handmaid\\'s Tale',\n",
    "    '\\'PAW Patrol\\'': 'PAW Patrol',\n",
    "    '\\'Loki\\'': 'Loki',\n",
    "    '\\'Jujutsu Kaisen\\'': 'Jujutsu Kaisen',\n",
    "    '\\'Dark\\'': 'Dark',\n",
    "    '\\'Dragon Ball Super\\'': 'Dragon Ball Super',\n",
    "    '\\'13 Reasons Why\\'': '13 Reasons Why',\n",
    "    '\\'The Big Bang Theory\\'': 'Big Bang Theory',\n",
    "    '\\'Westworld\\'': 'Westworld',\n",
    "    '\\'The Witcher\\'': 'The Witcher',\n",
    "    '\\'Squid Game\\'': 'Squid Game',\n",
    "    '\\'The Umbrella Academy\\'': 'Umbrella Academy',\n",
    "    '\\'American Horror Story\\'': 'American Horror Story',\n",
    "    'South Park': 'South Park',\n",
    "    '\\'Arrow\\'': 'Arrow',\n",
    "    '\\\\\\'[\\\"Grey\\\\\\\\\\\\\\'s Anatomy\\\"]\\\\\\']': 'Greys Anatomy',\n",
    "    '\\'Rick and Morty\\'': 'Rick and Morty',\n",
    "    '\\'The Falcon and The Winter Soldier\\'': 'Falcon and Winter Soldier',\n",
    "    '\\'The Flash\\'': 'The Flash',\n",
    "    'The Book of Boba Fett\\'': 'Book of Boba Fett',\n",
    "    '\\'Stranger Things\\'': 'Stranger Things',\n",
    "    '\\'WandaVision\\'': 'WandaVision',\n",
    "    '\\'Better Call Saul\\'': 'Better Call Saul',\n",
    "    '\\'Moon Knight\\'': 'Moon Knight',\n",
    "    '\\'SpongeBob SquarePants\\'': 'SpongeBob',\n",
    "    '\\'The Walking Dead\\'': 'Walking Dead',\n",
    "    '\\'Bridgerton\\'': 'Bridgerton',\n",
    "    '\\'Supernatural\\'': 'Supernatural',\n",
    "    '\\'Orange is the New Black\\'': 'Orange is the New Black',\n",
    "    '\\'Peaky Blinders\\'': 'Peaky Blinders',\n",
    "    '\\'The Boys\\'': 'The Boys',\n",
    "    '\\'Attack On Titan\\'': 'Attack On Titan',\n",
    "    '\\'Euphoria\\'': 'Euphoria',\n",
    "    '\\'Hawkeye\\'': 'Hawkeye',\n",
    "    '\\'Riverdale\\'': 'Riverdale',\n",
    "    '\\'Game of Thrones\\'': 'Game of Thrones',\n",
    "    '\\'Lucifer\\'': 'Lucifer',\n",
    "    '\\'My Hero Academia\\'': 'My Hero Academia',\n",
    "    '\\'Cobra Kai\\'': 'Cobra Kai',\n",
    "    '\\'Brooklyn Nine-Nine\\'': 'Brooklyn Nine-Nine',\n",
    "    '\\'Black Mirror\\'': 'Black Mirror',\n",
    "    '\\\\\\'[\\\"Marvel\\\\\\\\\\\\\\'s Agents Of S.H.I.E.L.D.\\\"]\\\\\\']': 'Agents of SHIELD',\n",
    "    '\\'The Wheel of Time\\'': 'The Wheel of Time',\n",
    "    '\\'Outlander\\'': 'Outlander',\n",
    "    '\\'The 100\\'': 'The 100'\n",
    "    # Add more mappings as needed\n",
    "}\n",
    "\n",
    "# Map the \"SHOW\" column to standardize labels\n",
    "df['SHOW'] = df['SHOW'].map(label_mapping)\n",
    "\n",
    "#and filter out Paw Patrol\n",
    "#df = df[df['SHOW'] != 'PAW Patrol']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "198872d3-c479-4f52-8b56-034e93e29ee7",
   "metadata": {},
   "source": [
    "### Analyze number of users in each subreddit for the purposes of filtering smaller subreddits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e3d53fb9-231a-49fd-998d-835fbadeda60",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Membership Size Statistics:\n",
      "count        49.000000\n",
      "mean      31467.816327\n",
      "std       34648.887054\n",
      "min        1064.000000\n",
      "25%       11616.000000\n",
      "50%       17019.000000\n",
      "75%       41028.000000\n",
      "max      205803.000000\n",
      "Name: NUM_MEMBERS, dtype: float64\n",
      "\n",
      "Smallest subreddits:\n",
      "                         SHOW  NUM_MEMBERS\n",
      "2       American Horror Story        15212\n",
      "8           Book of Boba Fett        14909\n",
      "22                    Lucifer        13811\n",
      "35                  SpongeBob        13258\n",
      "17              Greys Anatomy        13099\n",
      "42          The Wheel of Time        12231\n",
      "24                Money Heist        11779\n",
      "39                    The 100        11616\n",
      "15  Falcon and Winter Soldier        11230\n",
      "33                  Riverdale        10159\n",
      "0              13 Reasons Why        10104\n",
      "45                    Vikings         9488\n",
      "25                Moon Knight         9406\n",
      "29                  Outlander         9349\n",
      "9                  Bridgerton         8687\n",
      "3                       Arrow         8612\n",
      "28    Orange is the New Black         6995\n",
      "6             Big Bang Theory         6684\n",
      "19                    Hawkeye         4685\n",
      "30                 PAW Patrol         1064\n",
      "\n",
      "Largest subreddits:\n",
      "                  SHOW  NUM_MEMBERS\n",
      "16     Game of Thrones       205803\n",
      "43         The Witcher        94043\n",
      "32      Rick and Morty        88220\n",
      "27           One Piece        79579\n",
      "4      Attack On Titan        75248\n",
      "37     Stranger Things        69407\n",
      "10  Brooklyn Nine-Nine        60925\n",
      "40            The Boys        60307\n",
      "23         Mandalorian        57005\n",
      "5     Better Call Saul        49243\n",
      "26    My Hero Academia        46623\n",
      "13   Dragon Ball Super        43346\n",
      "48           Westworld        41028\n",
      "34          South Park        37020\n",
      "14            Euphoria        35740\n",
      "7         Black Mirror        31537\n",
      "44    Umbrella Academy        28202\n",
      "47         WandaVision        27378\n",
      "36          Squid Game        27283\n",
      "18     Handmaid's Tale        25708\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Subreddits below thresholds:\n",
      "  Below 10 members: 0 subreddits\n",
      "  Below 25 members: 0 subreddits\n",
      "  Below 50 members: 0 subreddits\n",
      "  Below 100 members: 0 subreddits\n",
      "  Below 200 members: 0 subreddits\n"
     ]
    }
   ],
   "source": [
    "#determine overall membership size, before filtering\n",
    "# Get the number of unique users per show/subreddit\n",
    "membership_size = df.groupby('SHOW')['USERNAME'].nunique().reset_index()\n",
    "membership_size.columns = ['SHOW', 'NUM_MEMBERS']\n",
    "\n",
    "# Sort by size\n",
    "membership_size = membership_size.sort_values('NUM_MEMBERS', ascending=False)\n",
    "\n",
    "# Display basic statistics\n",
    "print(\"Membership Size Statistics:\")\n",
    "print(membership_size['NUM_MEMBERS'].describe())\n",
    "print(\"\\nSmallest subreddits:\")\n",
    "print(membership_size.tail(20))\n",
    "print(\"\\nLargest subreddits:\")\n",
    "print(membership_size.head(20))\n",
    "\n",
    "# Visualize the distribution\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Histogram\n",
    "axes[0].hist(membership_size['NUM_MEMBERS'], bins=30, edgecolor='black')\n",
    "axes[0].set_xlabel('Number of Members')\n",
    "axes[0].set_ylabel('Number of Subreddits')\n",
    "axes[0].set_title('Distribution of Subreddit Membership Size')\n",
    "\n",
    "# Log scale histogram (useful if there's a wide range)\n",
    "axes[1].hist(membership_size['NUM_MEMBERS'], bins=30, edgecolor='black')\n",
    "axes[1].set_xlabel('Number of Members')\n",
    "axes[1].set_ylabel('Number of Subreddits')\n",
    "axes[1].set_title('Distribution (Log Scale)')\n",
    "axes[1].set_xscale('log')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Check how many subreddits fall below certain thresholds\n",
    "thresholds = [10, 25, 50, 100, 200]\n",
    "print(\"\\nSubreddits below thresholds:\")\n",
    "for threshold in thresholds:\n",
    "    count = (membership_size['NUM_MEMBERS'] < threshold).sum()\n",
    "    print(f\"  Below {threshold} members: {count} subreddits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c7bdb1c1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           count       mean         std  min  25%  50%  75%  \\\n",
      "SUBREDDIT                                                                     \n",
      "'r/13reasonswhy'         10105.0   4.383671   77.336270  1.0  1.0  1.0  3.0   \n",
      "'r/agentsofshield'        1460.0   7.662329   31.210802  1.0  1.0  2.0  6.0   \n",
      "'r/americanhorrorstory'  15213.0   3.745941   68.199789  1.0  1.0  1.0  3.0   \n",
      "'r/arrow'                 8613.0   4.995356   66.582585  1.0  1.0  2.0  4.0   \n",
      "'r/attackontitan'        24937.0   3.246341   68.996248  1.0  1.0  1.0  3.0   \n",
      "...                          ...        ...         ...  ...  ...  ...  ...   \n",
      "'r/wandavision'          27379.0   4.040834   79.994364  1.0  1.0  1.0  3.0   \n",
      "'r/westworld'            41029.0   3.276804   93.310161  1.0  1.0  1.0  2.0   \n",
      "'r/wheeloftime'          12232.0   7.772973  101.468004  1.0  1.0  2.0  5.0   \n",
      "'r/wiedzmin'              5514.0  11.447951  114.293566  1.0  1.0  2.0  5.0   \n",
      "'r/witcher'              80301.0   3.203721  116.006048  1.0  1.0  1.0  2.0   \n",
      "\n",
      "                             max  \n",
      "SUBREDDIT                         \n",
      "'r/13reasonswhy'          7684.0  \n",
      "'r/agentsofshield'         810.0  \n",
      "'r/americanhorrorstory'   8377.0  \n",
      "'r/arrow'                 6091.0  \n",
      "'r/attackontitan'        10782.0  \n",
      "...                          ...  \n",
      "'r/wandavision'          13018.0  \n",
      "'r/westworld'            18862.0  \n",
      "'r/wheeloftime'          11003.0  \n",
      "'r/wiedzmin'              7010.0  \n",
      "'r/witcher'              32602.0  \n",
      "\n",
      "[66 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "#get the distribution of raw_count by subreddit\n",
    "grouped_stats = df.groupby('SUBREDDIT')['RAW_COUNT'].describe()\n",
    "\n",
    "print(grouped_stats)\n",
    "\n",
    "#note: \"count\" here refers to the number of rows, ie. users who commented on the subreddit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4426d810-77ed-450b-9d88-b99fcf867490",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUBREDDIT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'r/freefolk'</th>\n",
       "      <td>130116.0</td>\n",
       "      <td>3.922938</td>\n",
       "      <td>238.640202</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/gameofthrones'</th>\n",
       "      <td>104893.0</td>\n",
       "      <td>2.723709</td>\n",
       "      <td>139.090621</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>45025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/rickandmorty'</th>\n",
       "      <td>84052.0</td>\n",
       "      <td>2.491291</td>\n",
       "      <td>118.856910</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>34404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/witcher'</th>\n",
       "      <td>80301.0</td>\n",
       "      <td>3.203721</td>\n",
       "      <td>116.006048</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/onepiece'</th>\n",
       "      <td>77357.0</td>\n",
       "      <td>3.969066</td>\n",
       "      <td>106.509507</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/strangerthings'</th>\n",
       "      <td>69408.0</td>\n",
       "      <td>2.835134</td>\n",
       "      <td>108.251214</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/brooklynninenine'</th>\n",
       "      <td>60926.0</td>\n",
       "      <td>2.725864</td>\n",
       "      <td>92.957432</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22908.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/theboys'</th>\n",
       "      <td>60308.0</td>\n",
       "      <td>3.701930</td>\n",
       "      <td>124.775209</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30595.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/bettercallsaul'</th>\n",
       "      <td>49244.0</td>\n",
       "      <td>4.335960</td>\n",
       "      <td>125.210529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27697.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/themandaloriantv'</th>\n",
       "      <td>48344.0</td>\n",
       "      <td>3.634825</td>\n",
       "      <td>106.844482</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23448.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/bokunoheroacademia'</th>\n",
       "      <td>46624.0</td>\n",
       "      <td>3.698460</td>\n",
       "      <td>94.294506</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/westworld'</th>\n",
       "      <td>41029.0</td>\n",
       "      <td>3.276804</td>\n",
       "      <td>93.310161</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/shingekinokyojin'</th>\n",
       "      <td>39027.0</td>\n",
       "      <td>3.969047</td>\n",
       "      <td>115.737061</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22795.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/southpark'</th>\n",
       "      <td>37022.0</td>\n",
       "      <td>2.776349</td>\n",
       "      <td>83.302864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/euphoria'</th>\n",
       "      <td>35741.0</td>\n",
       "      <td>4.825383</td>\n",
       "      <td>125.991237</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23724.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/dbz'</th>\n",
       "      <td>34961.0</td>\n",
       "      <td>3.629273</td>\n",
       "      <td>87.691882</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/blackmirror'</th>\n",
       "      <td>31538.0</td>\n",
       "      <td>2.803285</td>\n",
       "      <td>83.609913</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14833.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/titanfolk'</th>\n",
       "      <td>29959.0</td>\n",
       "      <td>7.137955</td>\n",
       "      <td>181.896831</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/wandavision'</th>\n",
       "      <td>27379.0</td>\n",
       "      <td>4.040834</td>\n",
       "      <td>79.994364</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/squidgame'</th>\n",
       "      <td>27285.0</td>\n",
       "      <td>3.447242</td>\n",
       "      <td>78.134910</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12869.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count      mean         std  min  25%  50%  75%  \\\n",
       "SUBREDDIT                                                                    \n",
       "'r/freefolk'            130116.0  3.922938  238.640202  1.0  1.0  1.0  3.0   \n",
       "'r/gameofthrones'       104893.0  2.723709  139.090621  1.0  1.0  1.0  2.0   \n",
       "'r/rickandmorty'         84052.0  2.491291  118.856910  1.0  1.0  1.0  2.0   \n",
       "'r/witcher'              80301.0  3.203721  116.006048  1.0  1.0  1.0  2.0   \n",
       "'r/onepiece'             77357.0  3.969066  106.509507  1.0  1.0  1.0  3.0   \n",
       "'r/strangerthings'       69408.0  2.835134  108.251214  1.0  1.0  1.0  2.0   \n",
       "'r/brooklynninenine'     60926.0  2.725864   92.957432  1.0  1.0  1.0  2.0   \n",
       "'r/theboys'              60308.0  3.701930  124.775209  1.0  1.0  1.0  3.0   \n",
       "'r/bettercallsaul'       49244.0  4.335960  125.210529  1.0  1.0  1.0  3.0   \n",
       "'r/themandaloriantv'     48344.0  3.634825  106.844482  1.0  1.0  1.0  3.0   \n",
       "'r/bokunoheroacademia'   46624.0  3.698460   94.294506  1.0  1.0  1.0  2.0   \n",
       "'r/westworld'            41029.0  3.276804   93.310161  1.0  1.0  1.0  2.0   \n",
       "'r/shingekinokyojin'     39027.0  3.969047  115.737061  1.0  1.0  1.0  3.0   \n",
       "'r/southpark'            37022.0  2.776349   83.302864  1.0  1.0  1.0  2.0   \n",
       "'r/euphoria'             35741.0  4.825383  125.991237  1.0  1.0  1.0  3.0   \n",
       "'r/dbz'                  34961.0  3.629273   87.691882  1.0  1.0  1.0  3.0   \n",
       "'r/blackmirror'          31538.0  2.803285   83.609913  1.0  1.0  1.0  2.0   \n",
       "'r/titanfolk'            29959.0  7.137955  181.896831  1.0  1.0  2.0  4.0   \n",
       "'r/wandavision'          27379.0  4.040834   79.994364  1.0  1.0  1.0  3.0   \n",
       "'r/squidgame'            27285.0  3.447242   78.134910  1.0  1.0  1.0  3.0   \n",
       "\n",
       "                            max  \n",
       "SUBREDDIT                        \n",
       "'r/freefolk'            85137.0  \n",
       "'r/gameofthrones'       45025.0  \n",
       "'r/rickandmorty'        34404.0  \n",
       "'r/witcher'             32602.0  \n",
       "'r/onepiece'            29503.0  \n",
       "'r/strangerthings'      28466.0  \n",
       "'r/brooklynninenine'    22908.0  \n",
       "'r/theboys'             30595.0  \n",
       "'r/bettercallsaul'      27697.0  \n",
       "'r/themandaloriantv'    23448.0  \n",
       "'r/bokunoheroacademia'  20163.0  \n",
       "'r/westworld'           18862.0  \n",
       "'r/shingekinokyojin'    22795.0  \n",
       "'r/southpark'           16010.0  \n",
       "'r/euphoria'            23724.0  \n",
       "'r/dbz'                 16286.0  \n",
       "'r/blackmirror'         14833.0  \n",
       "'r/titanfolk'           31316.0  \n",
       "'r/wandavision'         13018.0  \n",
       "'r/squidgame'           12869.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_stats.sort_values('count', ascending=False)[0:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a82f7c59-e68c-4d3b-a37c-a435d19615bb",
   "metadata": {},
   "source": [
    "### Analyze distribution of comment volume among users and filter low volume users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7198d608",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hoytlong\\AppData\\Local\\Temp\\ipykernel_35660\\3450740239.py:6: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  top_10_per_group = df.groupby('SUBREDDIT').apply(get_top_n)\n"
     ]
    }
   ],
   "source": [
    "# Function to get the top N commenters for each subreddit\n",
    "def get_top_n(group, n=10):\n",
    "    return group.nlargest(n, 'RAW_COUNT')\n",
    "\n",
    "# Group by 'Column_1', apply the function, and include 'Column_2' in the result\n",
    "top_10_per_group = df.groupby('SUBREDDIT').apply(get_top_n)\n",
    "\n",
    "# Reset index if necessary to clean up the DataFrame\n",
    "top_10_per_group.reset_index(drop=True, inplace=True)\n",
    "\n",
    "#top_10_per_group[0:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a36f85fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hoytlong\\AppData\\Local\\Temp\\ipykernel_35660\\3334479660.py:11: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  results = df.groupby('SUBREDDIT').apply(calculate_counts_and_percentage)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    66.000000\n",
       "mean     53.313301\n",
       "std       6.617981\n",
       "min      31.663327\n",
       "25%      50.415404\n",
       "50%      54.101189\n",
       "75%      57.379016\n",
       "max      66.191167\n",
       "Name: Perc_One_Counts, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for each subreddit, count how many users just leave 1 comment and calculate % of total commenters\n",
    "\n",
    "# Define a function to apply to each group\n",
    "def calculate_counts_and_percentage(group):\n",
    "    total_count = group.shape[0]  # Total number of rows in the group\n",
    "    count_of_one = (group['RAW_COUNT'] == 1).sum()  # Count of 'RAW_COUNT' == 1\n",
    "    percentage_of_one = (count_of_one / total_count) * 100  # Calculate the percentage\n",
    "    return pd.Series({'Count_of_One': count_of_one, 'Perc_One_Counts': percentage_of_one})\n",
    "\n",
    "# Group by 'Column_1' and apply the function\n",
    "results = df.groupby('SUBREDDIT').apply(calculate_counts_and_percentage)\n",
    "\n",
    "results[\"Perc_One_Counts\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b14d1155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(698767, 7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note: Based on the above analysis, it makes sense to filter out the \"None\" and \"AutoModerator\" usernames, \n",
    "# as well as users who only leave 1 comment -- these makeup anywhere from 31 to 66% of all commenters (mean = 53%)\n",
    "\n",
    "# Rows where 'USER' is 'None' or 'AutoModerator'\n",
    "condition1 = ~df['USERNAME'].isin(['None', 'AutoModerator'])\n",
    "# Rows where 'RAW_COUNT' is 1\n",
    "condition2 = df['RAW_COUNT'] != 1\n",
    "\n",
    "# Combine conditions using the logical AND operator\n",
    "combined_conditions = condition1 & condition2\n",
    "\n",
    "# Filter the DataFrame\n",
    "filtered_df = df[combined_conditions]\n",
    "\n",
    "# also remove nan values\n",
    "filtered_df = filtered_df.dropna(subset=['USERNAME'])\n",
    "\n",
    "filtered_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d85328fa-6c94-40a2-ab4f-9898a333ad9c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUBREDDIT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'r/freefolk'</th>\n",
       "      <td>55680.0</td>\n",
       "      <td>6.301455</td>\n",
       "      <td>53.831183</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12262.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/gameofthrones'</th>\n",
       "      <td>38588.0</td>\n",
       "      <td>4.518736</td>\n",
       "      <td>7.081431</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/onepiece'</th>\n",
       "      <td>34194.0</td>\n",
       "      <td>6.854126</td>\n",
       "      <td>13.987446</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>643.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/witcher'</th>\n",
       "      <td>32023.0</td>\n",
       "      <td>5.508010</td>\n",
       "      <td>23.406335</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/rickandmorty'</th>\n",
       "      <td>28416.0</td>\n",
       "      <td>4.200415</td>\n",
       "      <td>11.434191</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1685.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/theboys'</th>\n",
       "      <td>26906.0</td>\n",
       "      <td>5.919126</td>\n",
       "      <td>10.030946</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/strangerthings'</th>\n",
       "      <td>25322.0</td>\n",
       "      <td>4.906011</td>\n",
       "      <td>10.747862</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>654.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/bettercallsaul'</th>\n",
       "      <td>23072.0</td>\n",
       "      <td>6.919730</td>\n",
       "      <td>14.253127</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/brooklynninenine'</th>\n",
       "      <td>22152.0</td>\n",
       "      <td>4.712667</td>\n",
       "      <td>8.508304</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>633.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/themandaloriantv'</th>\n",
       "      <td>21138.0</td>\n",
       "      <td>5.916785</td>\n",
       "      <td>9.565670</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/bokunoheroacademia'</th>\n",
       "      <td>19092.0</td>\n",
       "      <td>6.533784</td>\n",
       "      <td>20.206437</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/euphoria'</th>\n",
       "      <td>17542.0</td>\n",
       "      <td>7.441683</td>\n",
       "      <td>15.736307</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>573.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/shingekinokyojin'</th>\n",
       "      <td>17323.0</td>\n",
       "      <td>6.373146</td>\n",
       "      <td>13.233159</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>789.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/westworld'</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>5.385529</td>\n",
       "      <td>8.978765</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/titanfolk'</th>\n",
       "      <td>16332.0</td>\n",
       "      <td>10.341905</td>\n",
       "      <td>25.087240</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>677.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/dbz'</th>\n",
       "      <td>15092.0</td>\n",
       "      <td>6.011728</td>\n",
       "      <td>15.227193</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>947.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/southpark'</th>\n",
       "      <td>13770.0</td>\n",
       "      <td>4.611184</td>\n",
       "      <td>6.323879</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/wandavision'</th>\n",
       "      <td>12841.0</td>\n",
       "      <td>6.469823</td>\n",
       "      <td>20.923385</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1984.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/thehandmaidstale'</th>\n",
       "      <td>12789.0</td>\n",
       "      <td>7.955665</td>\n",
       "      <td>16.711863</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>674.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'r/blackmirror'</th>\n",
       "      <td>12398.0</td>\n",
       "      <td>4.390869</td>\n",
       "      <td>5.922817</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count       mean        std  min  25%  50%  75%  \\\n",
       "SUBREDDIT                                                                   \n",
       "'r/freefolk'            55680.0   6.301455  53.831183  2.0  2.0  3.0  6.0   \n",
       "'r/gameofthrones'       38588.0   4.518736   7.081431  2.0  2.0  3.0  5.0   \n",
       "'r/onepiece'            34194.0   6.854126  13.987446  2.0  2.0  3.0  6.0   \n",
       "'r/witcher'             32023.0   5.508010  23.406335  2.0  2.0  3.0  5.0   \n",
       "'r/rickandmorty'        28416.0   4.200415  11.434191  2.0  2.0  3.0  4.0   \n",
       "'r/theboys'             26906.0   5.919126  10.030946  2.0  2.0  3.0  6.0   \n",
       "'r/strangerthings'      25322.0   4.906011  10.747862  2.0  2.0  3.0  5.0   \n",
       "'r/bettercallsaul'      23072.0   6.919730  14.253127  2.0  2.0  3.0  6.0   \n",
       "'r/brooklynninenine'    22152.0   4.712667   8.508304  2.0  2.0  3.0  5.0   \n",
       "'r/themandaloriantv'    21138.0   5.916785   9.565670  2.0  2.0  3.0  6.0   \n",
       "'r/bokunoheroacademia'  19092.0   6.533784  20.206437  2.0  2.0  3.0  5.0   \n",
       "'r/euphoria'            17542.0   7.441683  15.736307  2.0  2.0  3.0  7.0   \n",
       "'r/shingekinokyojin'    17323.0   6.373146  13.233159  2.0  2.0  3.0  6.0   \n",
       "'r/westworld'           17000.0   5.385529   8.978765  2.0  2.0  3.0  5.0   \n",
       "'r/titanfolk'           16332.0  10.341905  25.087240  2.0  2.0  4.0  9.0   \n",
       "'r/dbz'                 15092.0   6.011728  15.227193  2.0  2.0  3.0  6.0   \n",
       "'r/southpark'           13770.0   4.611184   6.323879  2.0  2.0  3.0  5.0   \n",
       "'r/wandavision'         12841.0   6.469823  20.923385  2.0  2.0  3.0  6.0   \n",
       "'r/thehandmaidstale'    12789.0   7.955665  16.711863  2.0  2.0  4.0  7.0   \n",
       "'r/blackmirror'         12398.0   4.390869   5.922817  2.0  2.0  3.0  5.0   \n",
       "\n",
       "                            max  \n",
       "SUBREDDIT                        \n",
       "'r/freefolk'            12262.0  \n",
       "'r/gameofthrones'         742.0  \n",
       "'r/onepiece'              643.0  \n",
       "'r/witcher'              3277.0  \n",
       "'r/rickandmorty'         1685.0  \n",
       "'r/theboys'               473.0  \n",
       "'r/strangerthings'        654.0  \n",
       "'r/bettercallsaul'        571.0  \n",
       "'r/brooklynninenine'      633.0  \n",
       "'r/themandaloriantv'      282.0  \n",
       "'r/bokunoheroacademia'   1349.0  \n",
       "'r/euphoria'              573.0  \n",
       "'r/shingekinokyojin'      789.0  \n",
       "'r/westworld'             277.0  \n",
       "'r/titanfolk'             677.0  \n",
       "'r/dbz'                   947.0  \n",
       "'r/southpark'             152.0  \n",
       "'r/wandavision'          1984.0  \n",
       "'r/thehandmaidstale'      674.0  \n",
       "'r/blackmirror'           205.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_stats = filtered_df.groupby('SUBREDDIT')['RAW_COUNT'].describe()\n",
    "filtered_stats.sort_values('count', ascending=False)[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "839f1d94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(318116, 7)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now produce a table that, for every username, lists all the subreddits to which they contribute along with raw count\n",
    "#and percent for that subreddit\n",
    "\n",
    "#basically, this just means subsetting to all usernames who appear in more than one subreddit\n",
    "\n",
    "# Count unique groups for each USERNAME\n",
    "subreddit_counts = filtered_df.groupby('USERNAME')['SUBREDDIT'].nunique()\n",
    "\n",
    "# Filter for usernames appearing in 2 or more subreddits\n",
    "multiple_subreddits = subreddit_counts[subreddit_counts >= 2].index\n",
    "\n",
    "# Filter the DataFrame based on the usernames identified\n",
    "new_df = filtered_df[filtered_df['USERNAME'].isin(multiple_subreddits)]\n",
    "\n",
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6deef8ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SUBREDDIT</th>\n",
       "      <th>SHOW</th>\n",
       "      <th>SUBREDDIT_TOTAL</th>\n",
       "      <th>USERNAME</th>\n",
       "      <th>RAW_COUNT</th>\n",
       "      <th>PERCENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>'r/mandalorian'</td>\n",
       "      <td>Mandalorian</td>\n",
       "      <td>32923</td>\n",
       "      <td>'Goliath_Gamer'</td>\n",
       "      <td>2</td>\n",
       "      <td>0.006075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>'r/mandalorian'</td>\n",
       "      <td>Mandalorian</td>\n",
       "      <td>32923</td>\n",
       "      <td>'GOpencyprep'</td>\n",
       "      <td>3</td>\n",
       "      <td>0.009112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>'r/mandalorian'</td>\n",
       "      <td>Mandalorian</td>\n",
       "      <td>32923</td>\n",
       "      <td>'F1r3spray'</td>\n",
       "      <td>5</td>\n",
       "      <td>0.015187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>'r/mandalorian'</td>\n",
       "      <td>Mandalorian</td>\n",
       "      <td>32923</td>\n",
       "      <td>'StPariah'</td>\n",
       "      <td>3</td>\n",
       "      <td>0.009112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>'r/mandalorian'</td>\n",
       "      <td>Mandalorian</td>\n",
       "      <td>32923</td>\n",
       "      <td>'cmmgreene'</td>\n",
       "      <td>4</td>\n",
       "      <td>0.012150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0        SUBREDDIT         SHOW  SUBREDDIT_TOTAL  \\\n",
       "5            5  'r/mandalorian'  Mandalorian            32923   \n",
       "6            6  'r/mandalorian'  Mandalorian            32923   \n",
       "8            8  'r/mandalorian'  Mandalorian            32923   \n",
       "12          12  'r/mandalorian'  Mandalorian            32923   \n",
       "20          20  'r/mandalorian'  Mandalorian            32923   \n",
       "\n",
       "           USERNAME  RAW_COUNT   PERCENT  \n",
       "5   'Goliath_Gamer'          2  0.006075  \n",
       "6     'GOpencyprep'          3  0.009112  \n",
       "8       'F1r3spray'          5  0.015187  \n",
       "12       'StPariah'          3  0.009112  \n",
       "20      'cmmgreene'          4  0.012150  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e61a3fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SUBREDDIT</th>\n",
       "      <th>SHOW</th>\n",
       "      <th>SUBREDDIT_TOTAL</th>\n",
       "      <th>USERNAME</th>\n",
       "      <th>RAW_COUNT</th>\n",
       "      <th>PERCENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>'r/mandalorian'</td>\n",
       "      <td>Mandalorian</td>\n",
       "      <td>32923</td>\n",
       "      <td>'F1r3spray'</td>\n",
       "      <td>5</td>\n",
       "      <td>0.015187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507774</th>\n",
       "      <td>507774</td>\n",
       "      <td>'r/bookofbobafett'</td>\n",
       "      <td>Book of Boba Fett</td>\n",
       "      <td>79529</td>\n",
       "      <td>'F1r3spray'</td>\n",
       "      <td>4</td>\n",
       "      <td>0.005030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045231</th>\n",
       "      <td>1045231</td>\n",
       "      <td>'r/themandaloriantv'</td>\n",
       "      <td>Mandalorian</td>\n",
       "      <td>175722</td>\n",
       "      <td>'F1r3spray'</td>\n",
       "      <td>3</td>\n",
       "      <td>0.001707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0             SUBREDDIT               SHOW  SUBREDDIT_TOTAL  \\\n",
       "8                 8       'r/mandalorian'        Mandalorian            32923   \n",
       "507774       507774    'r/bookofbobafett'  Book of Boba Fett            79529   \n",
       "1045231     1045231  'r/themandaloriantv'        Mandalorian           175722   \n",
       "\n",
       "            USERNAME  RAW_COUNT   PERCENT  \n",
       "8        'F1r3spray'          5  0.015187  \n",
       "507774   'F1r3spray'          4  0.005030  \n",
       "1045231  'F1r3spray'          3  0.001707  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "username_to_check = \"\\'F1r3spray\\'\"\n",
    "filtered_rows = new_df[new_df['USERNAME'] == username_to_check]\n",
    "\n",
    "filtered_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "837f9741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114585"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of unique users who comment on multiple subreddits in this dataset\n",
    "len(multiple_subreddits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b0731bb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SHOW</th>\n",
       "      <th>USERNAME</th>\n",
       "      <th>RAW_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>'-Archillion'</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>'-BINK2014-'</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>'-Dean_Winchester-'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>'-Jamberry-'</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>'-MassiveDynamic-'</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SHOW             USERNAME  RAW_COUNT\n",
       "0  13 Reasons Why        '-Archillion'          3\n",
       "1  13 Reasons Why         '-BINK2014-'          7\n",
       "2  13 Reasons Why  '-Dean_Winchester-'          2\n",
       "3  13 Reasons Why         '-Jamberry-'          9\n",
       "4  13 Reasons Why   '-MassiveDynamic-'         10"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now group the users by SHOW, rather than SUBREDDIT, so that we have total comments by show\n",
    "user_by_show = new_df.groupby(['SHOW', 'USERNAME']).agg({'RAW_COUNT': 'sum'}).reset_index()\n",
    "user_by_show.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "65d8e672",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SHOW</th>\n",
       "      <th>USERNAME</th>\n",
       "      <th>RAW_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>'69ingJamesFranco'</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>'Admirable-Ad-6275'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>'AlexTheRedditor97'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>'AmadeusKurisu'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>'BHarrop3079'</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              SHOW             USERNAME  RAW_COUNT\n",
       "18  13 Reasons Why   '69ingJamesFranco'         13\n",
       "35  13 Reasons Why  'Admirable-Ad-6275'          2\n",
       "46  13 Reasons Why  'AlexTheRedditor97'          2\n",
       "57  13 Reasons Why      'AmadeusKurisu'          2\n",
       "90  13 Reasons Why        'BHarrop3079'          9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shows = [\"Squid Game\", \"13 Reasons Why\"]\n",
    "selected_rows = user_by_show[user_by_show['SHOW'].isin(shows)]\n",
    "#selected_rows.head()\n",
    "\n",
    "#need to check how many usernames are common to both of these shows...\n",
    "show_counts = selected_rows.groupby('USERNAME')['SHOW'].nunique()\n",
    "\n",
    "# Filter for usernames appearing in 2 or more subreddits\n",
    "multiple_shows = show_counts[show_counts >= 2].index\n",
    "\n",
    "temp = selected_rows[selected_rows['USERNAME'].isin(multiple_shows)]\n",
    "\n",
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6e91519a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SHOW</th>\n",
       "      <th>USERNAME</th>\n",
       "      <th>RAW_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>'69ingJamesFranco'</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197952</th>\n",
       "      <td>Squid Game</td>\n",
       "      <td>'69ingJamesFranco'</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  SHOW            USERNAME  RAW_COUNT\n",
       "18      13 Reasons Why  '69ingJamesFranco'         13\n",
       "197952      Squid Game  '69ingJamesFranco'         13"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#temp[.RAW_COUNT.sum()]\n",
    "temp[temp[\"USERNAME\"]==\"\\'69ingJamesFranco\\'\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d3e2351-95a7-4ee5-9582-36cdf2abb831",
   "metadata": {},
   "source": [
    "### Analyze temporal dynamics of user activity (previous cells must be run to create \"filtered_df\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "544ccfbb-30b6-4593-ae34-b13ac23315c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3604140, 6)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import user comment data by subreddit\n",
    "df_time = pd.read_csv('Data/Subreddit_Username_Time_Data.csv', encoding='utf-8')\n",
    "df_time.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bbe078e6-d4e6-4f68-ac31-39b109d4dde1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SHOW</th>\n",
       "      <th>SUBREDDIT</th>\n",
       "      <th>USER_NAME</th>\n",
       "      <th>POST_LENGTH</th>\n",
       "      <th>TIME_STAMP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2528058</th>\n",
       "      <td>2365689</td>\n",
       "      <td>Squid Game</td>\n",
       "      <td>r/squidgame</td>\n",
       "      <td>-Arniox-</td>\n",
       "      <td>17</td>\n",
       "      <td>2021-10-19 21:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2568448</th>\n",
       "      <td>2381574</td>\n",
       "      <td>Squid Game</td>\n",
       "      <td>r/squidgame</td>\n",
       "      <td>-Arniox-</td>\n",
       "      <td>11</td>\n",
       "      <td>2021-11-16 14:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2568456</th>\n",
       "      <td>2381576</td>\n",
       "      <td>Squid Game</td>\n",
       "      <td>r/squidgame</td>\n",
       "      <td>-Arniox-</td>\n",
       "      <td>48</td>\n",
       "      <td>2021-11-16 14:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2568457</th>\n",
       "      <td>2381577</td>\n",
       "      <td>Squid Game</td>\n",
       "      <td>r/squidgame</td>\n",
       "      <td>-Arniox-</td>\n",
       "      <td>20</td>\n",
       "      <td>2021-11-16 14:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3034567</th>\n",
       "      <td>1665667</td>\n",
       "      <td>Moon Knight</td>\n",
       "      <td>r/moonknight</td>\n",
       "      <td>-Arniox-</td>\n",
       "      <td>15</td>\n",
       "      <td>2022-5-4 8:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3034589</th>\n",
       "      <td>1665686</td>\n",
       "      <td>Moon Knight</td>\n",
       "      <td>r/moonknight</td>\n",
       "      <td>-Arniox-</td>\n",
       "      <td>39</td>\n",
       "      <td>2022-5-4 8:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3158300</th>\n",
       "      <td>2460633</td>\n",
       "      <td>Stranger Things</td>\n",
       "      <td>r/strangerthings</td>\n",
       "      <td>-Arniox-</td>\n",
       "      <td>26</td>\n",
       "      <td>2022-6-26 4:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3436230</th>\n",
       "      <td>2389461</td>\n",
       "      <td>Squid Game</td>\n",
       "      <td>r/squidgame</td>\n",
       "      <td>-Arniox-</td>\n",
       "      <td>13</td>\n",
       "      <td>2022-9-18 5:03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0             SHOW         SUBREDDIT USER_NAME  POST_LENGTH  \\\n",
       "2528058     2365689       Squid Game       r/squidgame  -Arniox-           17   \n",
       "2568448     2381574       Squid Game       r/squidgame  -Arniox-           11   \n",
       "2568456     2381576       Squid Game       r/squidgame  -Arniox-           48   \n",
       "2568457     2381577       Squid Game       r/squidgame  -Arniox-           20   \n",
       "3034567     1665667      Moon Knight      r/moonknight  -Arniox-           15   \n",
       "3034589     1665686      Moon Knight      r/moonknight  -Arniox-           39   \n",
       "3158300     2460633  Stranger Things  r/strangerthings  -Arniox-           26   \n",
       "3436230     2389461       Squid Game       r/squidgame  -Arniox-           13   \n",
       "\n",
       "               TIME_STAMP  \n",
       "2528058  2021-10-19 21:11  \n",
       "2568448  2021-11-16 14:20  \n",
       "2568456  2021-11-16 14:25  \n",
       "2568457  2021-11-16 14:26  \n",
       "3034567     2022-5-4 8:05  \n",
       "3034589     2022-5-4 8:13  \n",
       "3158300    2022-6-26 4:37  \n",
       "3436230    2022-9-18 5:03  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_time[df_time[\"USER_NAME\"]=='-Arniox-']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3b9c05b9-3f32-4a94-b112-11519c5bf958",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 15036 users who contribute only to the Squid Game subreddit.\n",
      "There are 10985 users who contribute to Squid Game plus other subreddits.\n"
     ]
    }
   ],
   "source": [
    "#get list of users who commented only on SG and users who commented on SG and other shows\n",
    "user_by_subs = pd.read_csv('Data/User_Subreddits.csv', encoding='utf-8')\n",
    "\n",
    "#create lists to store usernames\n",
    "sg_only = []\n",
    "sg_plus = []\n",
    "\n",
    "for k in user_by_subs.index:\n",
    "    user = user_by_subs.loc[k, \"USERNAME\"]\n",
    "    user = re.sub(r'\\'', '', user)\n",
    "    \n",
    "    subs = re.sub(r'[\\[\\]\\'\\\"\\s]', '', user_by_subs.loc[k, \"SUBREDDIT\"])\n",
    "    subs = re.sub(r'r\\/', '', subs)\n",
    "    subs = re.split(',', subs)\n",
    "\n",
    "    if len(subs) > 1:\n",
    "        if 'squidgame' in subs:\n",
    "            sg_plus.append(user)   \n",
    "    else:\n",
    "        if 'squidgame' in subs:\n",
    "            sg_only.append(user)\n",
    "\n",
    "print(\"There are \" + str(len(sg_only)) + \" users who contribute only to the Squid Game subreddit.\")\n",
    "print(\"There are \" + str(len(sg_plus)) + \" users who contribute to Squid Game plus other subreddits.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0a43cfd9-4b21-4204-bb1c-27c406192599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125547, 6)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter overall time data down to the sg_plus usernames\n",
    "username_set = set(sg_plus)  # Convert to set for O(1) lookups\n",
    "filtered_df = df_time[df_time['USER_NAME'].isin(username_set)]\n",
    "filtered_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "704f05e8-9d70-421d-ba4a-43b5235fb19e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101461, 6)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now filter out the datapoints for Squid Game subreddit\n",
    "filtered_df = filtered_df[filtered_df['SHOW']!='Squid Game']\n",
    "filtered_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a15fadc2-4ab8-4f4f-a8b1-9ba7a04e79f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Convert the string timestamp to datetime\n",
    "filtered_df['DATE'] = pd.to_datetime(filtered_df['TIME_STAMP'], format='%Y-%m-%d %H:%M')\n",
    "\n",
    "# Step 2: Define the cutoff date\n",
    "cutoff_date = pd.to_datetime('2021-09-17')\n",
    "\n",
    "# Step 3: Split the dataframe into two based on the cutoff date\n",
    "before_cutoff = filtered_df[filtered_df['DATE'] < cutoff_date]\n",
    "after_cutoff = filtered_df[filtered_df['DATE'] >= cutoff_date]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0e887ebc-cc68-460a-834e-1218e8403a19",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top shows before cutoff:\n",
      "                                 SHOW  count percentage\n",
      "12                    Game of Thrones   9599     14.63%\n",
      "3                     Attack on Titan   6642     10.12%\n",
      "39                    The Mandalorian   3140      4.78%\n",
      "43                        The Witcher   2607      3.97%\n",
      "27                     Rick and Morty   2396      3.65%\n",
      "38                The Handmaid's Tale   2339      3.56%\n",
      "45                        WandaVision   2328      3.55%\n",
      "35                           The Boys   2298       3.5%\n",
      "5                        Black Mirror   2165       3.3%\n",
      "7                  Brooklyn Nine-Nine   2003      3.05%\n",
      "46                          Westworld   1989      3.03%\n",
      "36  The Falcon and the Winter Soldier   1852      2.82%\n",
      "17                               Loki   1738      2.65%\n",
      "40               The Umbrella Academy   1723      2.63%\n",
      "9                                Dark   1610      2.45%\n",
      "31                    Stranger Things   1609      2.45%\n",
      "21                   My Hero Academia   1445       2.2%\n",
      "8                           Cobra Kai   1276      1.94%\n",
      "22                          One Piece   1203      1.83%\n",
      "10                  Dragon Ball Super   1202      1.83%\n",
      "\n",
      "Top shows after cutoff:\n",
      "                     SHOW  count percentage\n",
      "11               Euphoria   4600     12.84%\n",
      "4        Better Call Saul   4483     12.51%\n",
      "36               The Boys   4254     11.87%\n",
      "31        Stranger Things   2306      6.43%\n",
      "35  The Book of Boba Fett   1941      5.42%\n",
      "22              One Piece   1793       5.0%\n",
      "3         Attack on Titan   1746      4.87%\n",
      "44            The Witcher   1593      4.45%\n",
      "41   The Umbrella Academy   1400      3.91%\n",
      "39    The Handmaid's Tale   1267      3.54%\n",
      "8               Cobra Kai   1171      3.27%\n",
      "6              Bridgerton   1025      2.86%\n",
      "20            Moon Knight    721      2.01%\n",
      "16       La Casa De Papel    660      1.84%\n",
      "12        Game of Thrones    634      1.77%\n",
      "43      The Wheel of Time    563      1.57%\n",
      "10      Dragon Ball Super    492      1.37%\n",
      "14                Hawkeye    437      1.22%\n",
      "47              Westworld    339      0.95%\n",
      "40        The Mandalorian    335      0.93%\n"
     ]
    }
   ],
   "source": [
    "#now get counts of subreddits for before and after\n",
    "before_counts = before_cutoff.groupby('SHOW').size()\n",
    "after_counts = after_cutoff.groupby('SHOW').size()\n",
    "\n",
    "# Step 2: Calculate total rows in each dataframe\n",
    "before_total = len(before_cutoff)\n",
    "after_total = len(after_cutoff)\n",
    "\n",
    "# Step 3: Calculate percentages\n",
    "before_percentages = before_counts / before_total * 100\n",
    "after_percentages = after_counts / after_total * 100\n",
    "\n",
    "# Step 4: Combine counts and percentages into DataFrames\n",
    "before_results = pd.DataFrame({\n",
    "    'count': before_counts,\n",
    "    'percentage': before_percentages\n",
    "}).reset_index()\n",
    "\n",
    "after_results = pd.DataFrame({\n",
    "    'count': after_counts,\n",
    "    'percentage': after_percentages\n",
    "}).reset_index()\n",
    "\n",
    "# Step 5: Sort by count in descending order\n",
    "before_results = before_results.sort_values('count', ascending=False)\n",
    "after_results = after_results.sort_values('count', ascending=False)\n",
    "\n",
    "# Format percentages to 2 decimal places\n",
    "before_results['percentage'] = before_results['percentage'].round(2)\n",
    "after_results['percentage'] = after_results['percentage'].round(2)\n",
    "\n",
    "# Add % symbol if desired\n",
    "before_results['percentage'] = before_results['percentage'].astype(str) + '%'\n",
    "after_results['percentage'] = after_results['percentage'].astype(str) + '%'\n",
    "\n",
    "# Display top 20 shows before cutoff\n",
    "print(\"Top shows before cutoff:\")\n",
    "print(before_results.head(20))\n",
    "\n",
    "# Display top 20 shows after cutoff\n",
    "print(\"\\n\" + \"Top shows after cutoff:\")\n",
    "print(after_results.head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "16e8a00f-6b92-4b21-a388-c7b5470008c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SHOW</th>\n",
       "      <th>SUBREDDIT</th>\n",
       "      <th>USER_NAME</th>\n",
       "      <th>POST_LENGTH</th>\n",
       "      <th>TIME_STAMP</th>\n",
       "      <th>DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>272551</td>\n",
       "      <td>Black Mirror</td>\n",
       "      <td>r/blackmirror</td>\n",
       "      <td>jennerality</td>\n",
       "      <td>53</td>\n",
       "      <td>2018-1-1 0:08</td>\n",
       "      <td>2018-01-01 00:08:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1291457</td>\n",
       "      <td>Game of Thrones</td>\n",
       "      <td>r/gameofthrones</td>\n",
       "      <td>Okichah</td>\n",
       "      <td>13</td>\n",
       "      <td>2018-1-1 2:16</td>\n",
       "      <td>2018-01-01 02:16:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>1291459</td>\n",
       "      <td>Game of Thrones</td>\n",
       "      <td>r/gameofthrones</td>\n",
       "      <td>RemindMeBot</td>\n",
       "      <td>71</td>\n",
       "      <td>2018-1-1 2:34</td>\n",
       "      <td>2018-01-01 02:34:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>272688</td>\n",
       "      <td>Black Mirror</td>\n",
       "      <td>r/blackmirror</td>\n",
       "      <td>dev1359</td>\n",
       "      <td>24</td>\n",
       "      <td>2018-1-1 4:03</td>\n",
       "      <td>2018-01-01 04:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>272709</td>\n",
       "      <td>Black Mirror</td>\n",
       "      <td>r/blackmirror</td>\n",
       "      <td>LiterallyKesha</td>\n",
       "      <td>77</td>\n",
       "      <td>2018-1-1 4:36</td>\n",
       "      <td>2018-01-01 04:36:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0             SHOW        SUBREDDIT       USER_NAME  \\\n",
       "10       272551     Black Mirror    r/blackmirror     jennerality   \n",
       "138     1291457  Game of Thrones  r/gameofthrones         Okichah   \n",
       "151     1291459  Game of Thrones  r/gameofthrones     RemindMeBot   \n",
       "233      272688     Black Mirror    r/blackmirror         dev1359   \n",
       "259      272709     Black Mirror    r/blackmirror  LiterallyKesha   \n",
       "\n",
       "     POST_LENGTH     TIME_STAMP                DATE  \n",
       "10            53  2018-1-1 0:08 2018-01-01 00:08:00  \n",
       "138           13  2018-1-1 2:16 2018-01-01 02:16:00  \n",
       "151           71  2018-1-1 2:34 2018-01-01 02:34:00  \n",
       "233           24  2018-1-1 4:03 2018-01-01 04:03:00  \n",
       "259           77  2018-1-1 4:36 2018-01-01 04:36:00  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "before_cutoff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5f72f49c-5c4e-4e51-9df8-e3d7fe822b29",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top shows before cutoff:\n",
      "                                 SHOW  count  unique_users show_percentage  \\\n",
      "12                    Game of Thrones   9599          1971          14.63%   \n",
      "3                     Attack on Titan   6642           766          10.12%   \n",
      "39                    The Mandalorian   3140           816           4.78%   \n",
      "43                        The Witcher   2607           657           3.97%   \n",
      "27                     Rick and Morty   2396           943           3.65%   \n",
      "38                The Handmaid's Tale   2339           365           3.56%   \n",
      "45                        WandaVision   2328           571           3.55%   \n",
      "35                           The Boys   2298           653            3.5%   \n",
      "5                        Black Mirror   2165           707            3.3%   \n",
      "7                  Brooklyn Nine-Nine   2003           603           3.05%   \n",
      "46                          Westworld   1989           462           3.03%   \n",
      "36  The Falcon and the Winter Soldier   1852           314           2.82%   \n",
      "17                               Loki   1738           436           2.65%   \n",
      "40               The Umbrella Academy   1723           429           2.63%   \n",
      "9                                Dark   1610           323           2.45%   \n",
      "31                    Stranger Things   1609           600           2.45%   \n",
      "21                   My Hero Academia   1445           351            2.2%   \n",
      "8                           Cobra Kai   1276           267           1.94%   \n",
      "22                          One Piece   1203           280           1.83%   \n",
      "10                  Dragon Ball Super   1202           240           1.83%   \n",
      "\n",
      "   user_percentage  \n",
      "12          27.98%  \n",
      "3           10.87%  \n",
      "39          11.58%  \n",
      "43           9.33%  \n",
      "27          13.39%  \n",
      "38           5.18%  \n",
      "45           8.11%  \n",
      "35           9.27%  \n",
      "5           10.04%  \n",
      "7            8.56%  \n",
      "46           6.56%  \n",
      "36           4.46%  \n",
      "17           6.19%  \n",
      "40           6.09%  \n",
      "9            4.59%  \n",
      "31           8.52%  \n",
      "21           4.98%  \n",
      "8            3.79%  \n",
      "22           3.98%  \n",
      "10           3.41%  \n",
      "\n",
      "Top shows after cutoff:\n",
      "                     SHOW  count  unique_users show_percentage user_percentage\n",
      "11               Euphoria   4600           682          12.84%          12.51%\n",
      "4        Better Call Saul   4483           827          12.51%          15.17%\n",
      "36               The Boys   4254          1022          11.87%          18.75%\n",
      "31        Stranger Things   2306           833           6.43%          15.28%\n",
      "35  The Book of Boba Fett   1941           377           5.42%           6.92%\n",
      "22              One Piece   1793           351            5.0%           6.44%\n",
      "3         Attack on Titan   1746           411           4.87%           7.54%\n",
      "44            The Witcher   1593           465           4.45%           8.53%\n",
      "41   The Umbrella Academy   1400           324           3.91%           5.94%\n",
      "39    The Handmaid's Tale   1267           189           3.54%           3.47%\n",
      "8               Cobra Kai   1171           291           3.27%           5.34%\n",
      "6              Bridgerton   1025           144           2.86%           2.64%\n",
      "20            Moon Knight    721           227           2.01%           4.17%\n",
      "16       La Casa De Papel    660           179           1.84%           3.28%\n",
      "12        Game of Thrones    634           273           1.77%           5.01%\n",
      "43      The Wheel of Time    563           115           1.57%           2.11%\n",
      "10      Dragon Ball Super    492           188           1.37%           3.45%\n",
      "14                Hawkeye    437           138           1.22%           2.53%\n",
      "47              Westworld    339           126           0.95%           2.31%\n",
      "40        The Mandalorian    335           164           0.93%           3.01%\n"
     ]
    }
   ],
   "source": [
    "# For before_cutoff_df\n",
    "before_results = before_cutoff.groupby('SHOW').agg(\n",
    "    count=('USER_NAME', 'size'),\n",
    "    unique_users=('USER_NAME', 'nunique')\n",
    ").reset_index()\n",
    "\n",
    "# Calculate total rows and unique users in before_cutoff_df\n",
    "before_total_rows = len(before_cutoff)\n",
    "before_total_users = before_cutoff['USER_NAME'].nunique()\n",
    "\n",
    "# Add percentage calculations\n",
    "before_results['show_percentage'] = (before_results['count'] / before_total_rows * 100).round(2)\n",
    "before_results['user_percentage'] = (before_results['unique_users'] / before_total_users * 100).round(2)\n",
    "\n",
    "# For after_cutoff_df\n",
    "after_results = after_cutoff.groupby('SHOW').agg(\n",
    "    count=('USER_NAME', 'size'),\n",
    "    unique_users=('USER_NAME', 'nunique')\n",
    ").reset_index()\n",
    "\n",
    "# Calculate total rows and unique users in after_cutoff_df\n",
    "after_total_rows = len(after_cutoff)\n",
    "after_total_users = after_cutoff['USER_NAME'].nunique()\n",
    "\n",
    "# Add percentage calculations\n",
    "after_results['show_percentage'] = (after_results['count'] / after_total_rows * 100).round(2)\n",
    "after_results['user_percentage'] = (after_results['unique_users'] / after_total_users * 100).round(2)\n",
    "\n",
    "# Sort results by count in descending order\n",
    "before_results = before_results.sort_values('count', ascending=False)\n",
    "after_results = after_results.sort_values('count', ascending=False)\n",
    "\n",
    "# Format percentages with % symbol\n",
    "for df in [before_results, after_results]:\n",
    "    df['show_percentage'] = df['show_percentage'].astype(str) + '%'\n",
    "    df['user_percentage'] = df['user_percentage'].astype(str) + '%'\n",
    "\n",
    "# Display top 20 shows before cutoff\n",
    "print(\"Top shows before cutoff:\")\n",
    "print(before_results.head(20))\n",
    "\n",
    "# Display top 20 shows after cutoff\n",
    "print(\"\\n\" + \"Top shows after cutoff:\")\n",
    "print(after_results.head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c831650c-ec15-48da-9c8b-a34f3dbdf1a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SHOW</th>\n",
       "      <th>count</th>\n",
       "      <th>unique_users</th>\n",
       "      <th>show_percentage</th>\n",
       "      <th>user_percentage</th>\n",
       "      <th>Activity_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Game of Thrones</td>\n",
       "      <td>9599</td>\n",
       "      <td>1971</td>\n",
       "      <td>14.63</td>\n",
       "      <td>27.98</td>\n",
       "      <td>18.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Attack on Titan</td>\n",
       "      <td>6642</td>\n",
       "      <td>766</td>\n",
       "      <td>10.12</td>\n",
       "      <td>10.87</td>\n",
       "      <td>10.345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>The Mandalorian</td>\n",
       "      <td>3140</td>\n",
       "      <td>816</td>\n",
       "      <td>4.78</td>\n",
       "      <td>11.58</td>\n",
       "      <td>6.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Rick and Morty</td>\n",
       "      <td>2396</td>\n",
       "      <td>943</td>\n",
       "      <td>3.65</td>\n",
       "      <td>13.39</td>\n",
       "      <td>6.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>The Witcher</td>\n",
       "      <td>2607</td>\n",
       "      <td>657</td>\n",
       "      <td>3.97</td>\n",
       "      <td>9.33</td>\n",
       "      <td>5.578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Black Mirror</td>\n",
       "      <td>2165</td>\n",
       "      <td>707</td>\n",
       "      <td>3.30</td>\n",
       "      <td>10.04</td>\n",
       "      <td>5.322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>The Boys</td>\n",
       "      <td>2298</td>\n",
       "      <td>653</td>\n",
       "      <td>3.50</td>\n",
       "      <td>9.27</td>\n",
       "      <td>5.231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>WandaVision</td>\n",
       "      <td>2328</td>\n",
       "      <td>571</td>\n",
       "      <td>3.55</td>\n",
       "      <td>8.11</td>\n",
       "      <td>4.918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Brooklyn Nine-Nine</td>\n",
       "      <td>2003</td>\n",
       "      <td>603</td>\n",
       "      <td>3.05</td>\n",
       "      <td>8.56</td>\n",
       "      <td>4.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Stranger Things</td>\n",
       "      <td>1609</td>\n",
       "      <td>600</td>\n",
       "      <td>2.45</td>\n",
       "      <td>8.52</td>\n",
       "      <td>4.271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  SHOW  count  unique_users  show_percentage  user_percentage  \\\n",
       "12     Game of Thrones   9599          1971            14.63            27.98   \n",
       "3      Attack on Titan   6642           766            10.12            10.87   \n",
       "39     The Mandalorian   3140           816             4.78            11.58   \n",
       "27      Rick and Morty   2396           943             3.65            13.39   \n",
       "43         The Witcher   2607           657             3.97             9.33   \n",
       "5         Black Mirror   2165           707             3.30            10.04   \n",
       "35            The Boys   2298           653             3.50             9.27   \n",
       "45         WandaVision   2328           571             3.55             8.11   \n",
       "7   Brooklyn Nine-Nine   2003           603             3.05             8.56   \n",
       "31     Stranger Things   1609           600             2.45             8.52   \n",
       "\n",
       "    Activity_Score  \n",
       "12          18.635  \n",
       "3           10.345  \n",
       "39           6.820  \n",
       "27           6.572  \n",
       "43           5.578  \n",
       "5            5.322  \n",
       "35           5.231  \n",
       "45           4.918  \n",
       "7            4.703  \n",
       "31           4.271  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "before_results['show_percentage'] = before_results['show_percentage'].str.replace('%', '').astype(float)\n",
    "before_results['user_percentage'] = before_results['user_percentage'].str.replace('%', '').astype(float)\n",
    "\n",
    "#Calculate an activity score that weights comment intensity and community overlap\n",
    "before_results['Activity_Score'] = (0.7 * before_results['show_percentage']) + (0.3 * before_results['user_percentage'])\n",
    "top_before = before_results.nlargest(10, 'Activity_Score')\n",
    "top_before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c8dd82dd-21de-4be2-ae63-13d13d475ec2",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SHOW</th>\n",
       "      <th>count</th>\n",
       "      <th>unique_users</th>\n",
       "      <th>show_percentage</th>\n",
       "      <th>user_percentage</th>\n",
       "      <th>Activity_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>The Boys</td>\n",
       "      <td>4254</td>\n",
       "      <td>1022</td>\n",
       "      <td>11.87</td>\n",
       "      <td>18.75</td>\n",
       "      <td>13.934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Better Call Saul</td>\n",
       "      <td>4483</td>\n",
       "      <td>827</td>\n",
       "      <td>12.51</td>\n",
       "      <td>15.17</td>\n",
       "      <td>13.308</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Euphoria</td>\n",
       "      <td>4600</td>\n",
       "      <td>682</td>\n",
       "      <td>12.84</td>\n",
       "      <td>12.51</td>\n",
       "      <td>12.741</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Stranger Things</td>\n",
       "      <td>2306</td>\n",
       "      <td>833</td>\n",
       "      <td>6.43</td>\n",
       "      <td>15.28</td>\n",
       "      <td>9.085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>The Book of Boba Fett</td>\n",
       "      <td>1941</td>\n",
       "      <td>377</td>\n",
       "      <td>5.42</td>\n",
       "      <td>6.92</td>\n",
       "      <td>5.870</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>The Witcher</td>\n",
       "      <td>1593</td>\n",
       "      <td>465</td>\n",
       "      <td>4.45</td>\n",
       "      <td>8.53</td>\n",
       "      <td>5.674</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Attack on Titan</td>\n",
       "      <td>1746</td>\n",
       "      <td>411</td>\n",
       "      <td>4.87</td>\n",
       "      <td>7.54</td>\n",
       "      <td>5.671</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>One Piece</td>\n",
       "      <td>1793</td>\n",
       "      <td>351</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.44</td>\n",
       "      <td>5.432</td>\n",
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       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>The Umbrella Academy</td>\n",
       "      <td>1400</td>\n",
       "      <td>324</td>\n",
       "      <td>3.91</td>\n",
       "      <td>5.94</td>\n",
       "      <td>4.519</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cobra Kai</td>\n",
       "      <td>1171</td>\n",
       "      <td>291</td>\n",
       "      <td>3.27</td>\n",
       "      <td>5.34</td>\n",
       "      <td>3.891</td>\n",
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       "                     SHOW  count  unique_users  show_percentage  \\\n",
       "36               The Boys   4254          1022            11.87   \n",
       "4        Better Call Saul   4483           827            12.51   \n",
       "11               Euphoria   4600           682            12.84   \n",
       "31        Stranger Things   2306           833             6.43   \n",
       "35  The Book of Boba Fett   1941           377             5.42   \n",
       "44            The Witcher   1593           465             4.45   \n",
       "3         Attack on Titan   1746           411             4.87   \n",
       "22              One Piece   1793           351             5.00   \n",
       "41   The Umbrella Academy   1400           324             3.91   \n",
       "8               Cobra Kai   1171           291             3.27   \n",
       "\n",
       "    user_percentage  Activity_Score  \n",
       "36            18.75          13.934  \n",
       "4             15.17          13.308  \n",
       "11            12.51          12.741  \n",
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     "execution_count": 47,
     "metadata": {},
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   "source": [
    "after_results['show_percentage'] = after_results['show_percentage'].str.replace('%', '').astype(float)\n",
    "after_results['user_percentage'] = after_results['user_percentage'].str.replace('%', '').astype(float)\n",
    "\n",
    "#Calculate an activity score that weights comment intensity and community overlap\n",
    "after_results['Activity_Score'] = (0.7 * after_results['show_percentage']) + (0.3 * after_results['user_percentage'])\n",
    "top_after = after_results.nlargest(10, 'Activity_Score')\n",
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   "cell_type": "markdown",
   "id": "d2b3d66e-8006-4d6f-bb9d-8226004e7d34",
   "metadata": {},
   "source": [
    "### Graph the results of preceding analysis (Figure 11)"
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   "cell_type": "code",
   "execution_count": 58,
   "id": "fc377732-5293-4f37-bf52-79c228a712af",
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User Activity Flow\"},\"font\":{\"size\":16},\"width\":1200,\"height\":800},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('05bdd578-786e-4132-9f78-c2f5eec3ccb4');\n",
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       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "    x.observe(outputEl, {childList: true});\n",
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       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "import pandas as pd\n",
    "\n",
    "# Assuming you have your top 10 before and after dataframes\n",
    "#top_before = df.nlargest(10, 'Activity_Score_Before')\n",
    "#top_after = df.nlargest(10, 'Activity_Score_After')\n",
    "\n",
    "# Create the Sankey diagram data\n",
    "def create_subreddit_flow_chart(top_before, top_after):\n",
    "    # Create node labels\n",
    "    before_labels = [f\"{sub}\" for sub in top_before['SHOW'][:10]]\n",
    "    after_labels = [f\"{sub}\" for sub in top_after['SHOW'][:10]]\n",
    "    \n",
    "    # All nodes: before subreddits + SquidGame + after subreddits\n",
    "    all_labels = before_labels + [\"SquidGame\"] + after_labels\n",
    "    \n",
    "    # Create source, target, and value lists for the flows\n",
    "    source = []\n",
    "    target = []\n",
    "    value = []\n",
    "    \n",
    "    # Flows from before subreddits to SquidGame\n",
    "    squidgame_index = len(before_labels)  # Index of SquidGame in the node list\n",
    "    \n",
    "    for i, (sub, row) in enumerate(top_before.head(10).iterrows()):\n",
    "        source.append(i)  # Index of before subreddit\n",
    "        target.append(squidgame_index)  # Index of SquidGame\n",
    "        value.append(row['Activity_Score'])\n",
    "    \n",
    "    # Flows from SquidGame to after subreddits\n",
    "    after_start_index = squidgame_index + 1\n",
    "    \n",
    "    for i, (sub, row) in enumerate(top_after.head(10).iterrows()):\n",
    "        source.append(squidgame_index)  # Index of SquidGame\n",
    "        target.append(after_start_index + i)  # Index of after subreddit\n",
    "        value.append(row['Activity_Score'])\n",
    "    \n",
    "    # Create the Sankey diagram\n",
    "    fig = go.Figure(data=[go.Sankey(\n",
    "        node=dict(\n",
    "            pad=15,\n",
    "            thickness=20,\n",
    "            line=dict(color=\"black\", width=0.5),\n",
    "            label=all_labels,\n",
    "            color=[\"blue\"]*len(before_labels) + [\"red\"] + [\"green\"]*len(after_labels)\n",
    "        ),\n",
    "        link=dict(\n",
    "            source=source,\n",
    "            target=target,\n",
    "            value=value,\n",
    "            color=\"rgba(100, 150, 200, 0.3)\"\n",
    "        )\n",
    "    )])\n",
    "    \n",
    "    fig.update_layout(\n",
    "        title_text=\"SquidGame User Activity Flow\",\n",
    "        font_size=16,\n",
    "        width=1200,\n",
    "        height=800\n",
    "    )\n",
    "    \n",
    "    return fig\n",
    "\n",
    "# Usage:\n",
    "fig = create_subreddit_flow_chart(top_before, top_after)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d99b2ab9-ae9f-4749-af7a-3e4629844b5c",
   "metadata": {},
   "source": [
    "### Calculate ratio of \"only posters\" to \"other posters\" for every subreddit in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1d8a0d62-1f38-48ee-9859-1fcd1b2686cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get list of users and subreddits they posted to\n",
    "user_by_subs = pd.read_csv('Data/User_Subreddits.csv', encoding='utf-8')\n",
    "subs_list = list(set(df_time['SUBREDDIT'].to_list()))\n",
    "\n",
    "#dictionary to store output\n",
    "results = {}\n",
    "\n",
    "for subreddit in subs_list:\n",
    "    #create lists to store usernames\n",
    "    only = []\n",
    "    other = []\n",
    "    \n",
    "    for k in user_by_subs.index:\n",
    "        user = user_by_subs.loc[k, \"USERNAME\"]\n",
    "        user = re.sub(r'\\'', '', user)\n",
    "        \n",
    "        subs = re.sub(r'[\\[\\]\\'\\\"\\s]', '', user_by_subs.loc[k, \"SUBREDDIT\"])\n",
    "        #subs = re.sub(r'r\\/', '', subs)\n",
    "        subs = re.split(',', subs)\n",
    "    \n",
    "        if len(subs) > 1:\n",
    "            if subreddit in subs:\n",
    "                other.append(user)   \n",
    "        else:\n",
    "            if subreddit in subs:\n",
    "                only.append(user)\n",
    "\n",
    "    results[subreddit] = {'subreddit': subreddit, 'num_only': len(only), 'num_other': len(other)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "edd35c82-426e-496d-8cf2-c8a30c6abe85",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "results_df = pd.DataFrame.from_dict(results, orient='index')\n",
    "results_df = results_df[['num_only', 'num_other']]\n",
    "results_df['ratio'] = results_df['num_only']/results_df['num_other']\n",
    "results_df = results_df.sort_values('ratio', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9d96d4f4-8e7b-48f5-93ce-14d4b4db165a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>num_only</th>\n",
       "      <th>num_other</th>\n",
       "      <th>ratio</th>\n",
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       "      <th>r/pawpatrol</th>\n",
       "      <td>808</td>\n",
       "      <td>216</td>\n",
       "      <td>3.740741</td>\n",
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       "      <td>3835</td>\n",
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       "      <th>r/onepiece</th>\n",
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       "      <td>22416</td>\n",
       "      <td>2.303712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/wheeloftime</th>\n",
       "      <td>8103</td>\n",
       "      <td>3607</td>\n",
       "      <td>2.246465</td>\n",
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       "      <th>r/euphoria</th>\n",
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       "      <td>10556</td>\n",
       "      <td>2.195718</td>\n",
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       "      <th>r/outlander</th>\n",
       "      <td>5830</td>\n",
       "      <td>3025</td>\n",
       "      <td>1.927273</td>\n",
       "    </tr>\n",
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       "      <th>r/greysanatomy</th>\n",
       "      <td>8082</td>\n",
       "      <td>4292</td>\n",
       "      <td>1.883038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/bridgertonnetflix</th>\n",
       "      <td>5276</td>\n",
       "      <td>2957</td>\n",
       "      <td>1.784241</td>\n",
       "    </tr>\n",
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       "      <th>r/lacasadepapel</th>\n",
       "      <td>7110</td>\n",
       "      <td>3987</td>\n",
       "      <td>1.783296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/bigbangtheory</th>\n",
       "      <td>3891</td>\n",
       "      <td>2459</td>\n",
       "      <td>1.582351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/peakyblinders</th>\n",
       "      <td>8999</td>\n",
       "      <td>5902</td>\n",
       "      <td>1.524737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/bokunoheroacademia</th>\n",
       "      <td>26820</td>\n",
       "      <td>17892</td>\n",
       "      <td>1.498994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/squidgame</th>\n",
       "      <td>15036</td>\n",
       "      <td>10985</td>\n",
       "      <td>1.368776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/bettercallsaul</th>\n",
       "      <td>27116</td>\n",
       "      <td>19867</td>\n",
       "      <td>1.364876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/dark</th>\n",
       "      <td>9813</td>\n",
       "      <td>7418</td>\n",
       "      <td>1.322863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/the100</th>\n",
       "      <td>6289</td>\n",
       "      <td>4765</td>\n",
       "      <td>1.319832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/supernatural</th>\n",
       "      <td>8439</td>\n",
       "      <td>6629</td>\n",
       "      <td>1.273043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/dbz</th>\n",
       "      <td>18584</td>\n",
       "      <td>14962</td>\n",
       "      <td>1.242080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/twd</th>\n",
       "      <td>1000</td>\n",
       "      <td>821</td>\n",
       "      <td>1.218027</td>\n",
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       "    <tr>\n",
       "      <th>r/jujutsukaisen</th>\n",
       "      <td>8395</td>\n",
       "      <td>6938</td>\n",
       "      <td>1.210003</td>\n",
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      "text/plain": [
       "                      num_only  num_other     ratio\n",
       "r/pawpatrol                808        216  3.740741\n",
       "r/spongebob               8854       3835  2.308735\n",
       "r/onepiece               51640      22416  2.303712\n",
       "r/wheeloftime             8103       3607  2.246465\n",
       "r/euphoria               23178      10556  2.195718\n",
       "r/outlander               5830       3025  1.927273\n",
       "r/greysanatomy            8082       4292  1.883038\n",
       "r/bridgertonnetflix       5276       2957  1.784241\n",
       "r/lacasadepapel           7110       3987  1.783296\n",
       "r/bigbangtheory           3891       2459  1.582351\n",
       "r/peakyblinders           8999       5902  1.524737\n",
       "r/bokunoheroacademia     26820      17892  1.498994\n",
       "r/squidgame              15036      10985  1.368776\n",
       "r/bettercallsaul         27116      19867  1.364876\n",
       "r/dark                    9813       7418  1.322863\n",
       "r/the100                  6289       4765  1.319832\n",
       "r/supernatural            8439       6629  1.273043\n",
       "r/dbz                    18584      14962  1.242080\n",
       "r/twd                     1000        821  1.218027\n",
       "r/jujutsukaisen           8395       6938  1.210003"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eb22b236-1426-4216-9f7f-16a6a353bf76",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_only</th>\n",
       "      <th>num_other</th>\n",
       "      <th>ratio</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>r/freefolk</th>\n",
       "      <td>59058</td>\n",
       "      <td>64681</td>\n",
       "      <td>0.913066</td>\n",
       "      <td>123739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/gameofthrones</th>\n",
       "      <td>46159</td>\n",
       "      <td>53467</td>\n",
       "      <td>0.863318</td>\n",
       "      <td>99626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/rickandmorty</th>\n",
       "      <td>42404</td>\n",
       "      <td>37659</td>\n",
       "      <td>1.125999</td>\n",
       "      <td>80063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/witcher</th>\n",
       "      <td>41424</td>\n",
       "      <td>35334</td>\n",
       "      <td>1.172355</td>\n",
       "      <td>76758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/onepiece</th>\n",
       "      <td>51640</td>\n",
       "      <td>22416</td>\n",
       "      <td>2.303712</td>\n",
       "      <td>74056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/strangerthings</th>\n",
       "      <td>32622</td>\n",
       "      <td>33563</td>\n",
       "      <td>0.971963</td>\n",
       "      <td>66185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/brooklynninenine</th>\n",
       "      <td>30209</td>\n",
       "      <td>27716</td>\n",
       "      <td>1.089948</td>\n",
       "      <td>57925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/theboys</th>\n",
       "      <td>24626</td>\n",
       "      <td>33123</td>\n",
       "      <td>0.743471</td>\n",
       "      <td>57749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/bettercallsaul</th>\n",
       "      <td>27116</td>\n",
       "      <td>19867</td>\n",
       "      <td>1.364876</td>\n",
       "      <td>46983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/themandaloriantv</th>\n",
       "      <td>18729</td>\n",
       "      <td>27513</td>\n",
       "      <td>0.680733</td>\n",
       "      <td>46242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/bokunoheroacademia</th>\n",
       "      <td>26820</td>\n",
       "      <td>17892</td>\n",
       "      <td>1.498994</td>\n",
       "      <td>44712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/westworld</th>\n",
       "      <td>17868</td>\n",
       "      <td>20941</td>\n",
       "      <td>0.853254</td>\n",
       "      <td>38809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/shingekinokyojin</th>\n",
       "      <td>16084</td>\n",
       "      <td>21222</td>\n",
       "      <td>0.757893</td>\n",
       "      <td>37306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/southpark</th>\n",
       "      <td>19166</td>\n",
       "      <td>15929</td>\n",
       "      <td>1.203214</td>\n",
       "      <td>35095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/euphoria</th>\n",
       "      <td>23178</td>\n",
       "      <td>10556</td>\n",
       "      <td>2.195718</td>\n",
       "      <td>33734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/dbz</th>\n",
       "      <td>18584</td>\n",
       "      <td>14962</td>\n",
       "      <td>1.242080</td>\n",
       "      <td>33546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/blackmirror</th>\n",
       "      <td>14596</td>\n",
       "      <td>15300</td>\n",
       "      <td>0.953987</td>\n",
       "      <td>29896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/titanfolk</th>\n",
       "      <td>12313</td>\n",
       "      <td>16316</td>\n",
       "      <td>0.754658</td>\n",
       "      <td>28629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/wandavision</th>\n",
       "      <td>9813</td>\n",
       "      <td>16349</td>\n",
       "      <td>0.600220</td>\n",
       "      <td>26162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/squidgame</th>\n",
       "      <td>15036</td>\n",
       "      <td>10985</td>\n",
       "      <td>1.368776</td>\n",
       "      <td>26021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      num_only  num_other     ratio   total\n",
       "r/freefolk               59058      64681  0.913066  123739\n",
       "r/gameofthrones          46159      53467  0.863318   99626\n",
       "r/rickandmorty           42404      37659  1.125999   80063\n",
       "r/witcher                41424      35334  1.172355   76758\n",
       "r/onepiece               51640      22416  2.303712   74056\n",
       "r/strangerthings         32622      33563  0.971963   66185\n",
       "r/brooklynninenine       30209      27716  1.089948   57925\n",
       "r/theboys                24626      33123  0.743471   57749\n",
       "r/bettercallsaul         27116      19867  1.364876   46983\n",
       "r/themandaloriantv       18729      27513  0.680733   46242\n",
       "r/bokunoheroacademia     26820      17892  1.498994   44712\n",
       "r/westworld              17868      20941  0.853254   38809\n",
       "r/shingekinokyojin       16084      21222  0.757893   37306\n",
       "r/southpark              19166      15929  1.203214   35095\n",
       "r/euphoria               23178      10556  2.195718   33734\n",
       "r/dbz                    18584      14962  1.242080   33546\n",
       "r/blackmirror            14596      15300  0.953987   29896\n",
       "r/titanfolk              12313      16316  0.754658   28629\n",
       "r/wandavision             9813      16349  0.600220   26162\n",
       "r/squidgame              15036      10985  1.368776   26021"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#look at top 30 subreddits by total comments\n",
    "results_df['total'] = results_df['num_only'] + results_df['num_other']\n",
    "results_df.sort_values('total', ascending=False)[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "742314ae-853d-4c5f-a0c7-40b1488e6024",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_only</th>\n",
       "      <th>num_other</th>\n",
       "      <th>ratio</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>r/13reasonswhy</th>\n",
       "      <td>5111</td>\n",
       "      <td>4455</td>\n",
       "      <td>1.147250</td>\n",
       "      <td>9566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/vikingstv</th>\n",
       "      <td>4707</td>\n",
       "      <td>4363</td>\n",
       "      <td>1.078845</td>\n",
       "      <td>9070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/moonknight</th>\n",
       "      <td>4539</td>\n",
       "      <td>4480</td>\n",
       "      <td>1.013170</td>\n",
       "      <td>9019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/outlander</th>\n",
       "      <td>5830</td>\n",
       "      <td>3025</td>\n",
       "      <td>1.927273</td>\n",
       "      <td>8855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/arrow</th>\n",
       "      <td>2316</td>\n",
       "      <td>5920</td>\n",
       "      <td>0.391216</td>\n",
       "      <td>8236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/bridgertonnetflix</th>\n",
       "      <td>5276</td>\n",
       "      <td>2957</td>\n",
       "      <td>1.784241</td>\n",
       "      <td>8233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/loki</th>\n",
       "      <td>3167</td>\n",
       "      <td>4405</td>\n",
       "      <td>0.718956</td>\n",
       "      <td>7572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/orangeisthenewblack</th>\n",
       "      <td>3135</td>\n",
       "      <td>3477</td>\n",
       "      <td>0.901639</td>\n",
       "      <td>6612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/bigbangtheory</th>\n",
       "      <td>3891</td>\n",
       "      <td>2459</td>\n",
       "      <td>1.582351</td>\n",
       "      <td>6350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/c137</th>\n",
       "      <td>2722</td>\n",
       "      <td>3113</td>\n",
       "      <td>0.874398</td>\n",
       "      <td>5835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/wiedzmin</th>\n",
       "      <td>1932</td>\n",
       "      <td>3338</td>\n",
       "      <td>0.578790</td>\n",
       "      <td>5270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/hawkeye</th>\n",
       "      <td>1588</td>\n",
       "      <td>2889</td>\n",
       "      <td>0.549671</td>\n",
       "      <td>4477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/coconutsandtreason</th>\n",
       "      <td>1425</td>\n",
       "      <td>2639</td>\n",
       "      <td>0.539977</td>\n",
       "      <td>4064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/piratefolk</th>\n",
       "      <td>1365</td>\n",
       "      <td>2651</td>\n",
       "      <td>0.514900</td>\n",
       "      <td>4016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/theflash</th>\n",
       "      <td>1495</td>\n",
       "      <td>1488</td>\n",
       "      <td>1.004704</td>\n",
       "      <td>2983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/twd</th>\n",
       "      <td>1000</td>\n",
       "      <td>821</td>\n",
       "      <td>1.218027</td>\n",
       "      <td>1821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/agentsofshield</th>\n",
       "      <td>697</td>\n",
       "      <td>712</td>\n",
       "      <td>0.978933</td>\n",
       "      <td>1409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/fandomnatural</th>\n",
       "      <td>456</td>\n",
       "      <td>702</td>\n",
       "      <td>0.649573</td>\n",
       "      <td>1158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/pawpatrol</th>\n",
       "      <td>808</td>\n",
       "      <td>216</td>\n",
       "      <td>3.740741</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r/onepiecepowerscaling</th>\n",
       "      <td>320</td>\n",
       "      <td>640</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        num_only  num_other     ratio  total\n",
       "r/13reasonswhy              5111       4455  1.147250   9566\n",
       "r/vikingstv                 4707       4363  1.078845   9070\n",
       "r/moonknight                4539       4480  1.013170   9019\n",
       "r/outlander                 5830       3025  1.927273   8855\n",
       "r/arrow                     2316       5920  0.391216   8236\n",
       "r/bridgertonnetflix         5276       2957  1.784241   8233\n",
       "r/loki                      3167       4405  0.718956   7572\n",
       "r/orangeisthenewblack       3135       3477  0.901639   6612\n",
       "r/bigbangtheory             3891       2459  1.582351   6350\n",
       "r/c137                      2722       3113  0.874398   5835\n",
       "r/wiedzmin                  1932       3338  0.578790   5270\n",
       "r/hawkeye                   1588       2889  0.549671   4477\n",
       "r/coconutsandtreason        1425       2639  0.539977   4064\n",
       "r/piratefolk                1365       2651  0.514900   4016\n",
       "r/theflash                  1495       1488  1.004704   2983\n",
       "r/twd                       1000        821  1.218027   1821\n",
       "r/agentsofshield             697        712  0.978933   1409\n",
       "r/fandomnatural              456        702  0.649573   1158\n",
       "r/pawpatrol                  808        216  3.740741   1024\n",
       "r/onepiecepowerscaling       320        640  0.500000    960"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.sort_values('total', ascending=False)[-20:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b7dd5137-b72d-43b9-b935-316d08ad0e36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23345.469696969696"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(results_df['total'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfb34288-f368-4093-92f0-6358929b8183",
   "metadata": {},
   "source": [
    "### Subreddit Size Analysis (Global Super Hits Only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d97e68eb-0823-481b-a003-e16769b2a1ea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SHOW COMMENT COUNTS\n",
      "============================================================\n",
      "Total shows: 49\n",
      "Total comments: 12,648,198\n",
      "\n",
      "Comment count range: 13,544 to 2,144,592\n",
      "Mean: 258,126\n",
      "Median: 130,449\n",
      "\n",
      "Top 10 shows by comment count:\n",
      "             SHOW  NUM_COMMENTS\n",
      "  Game of Thrones       2144592\n",
      "        One Piece       1622654\n",
      "  Attack On Titan        994408\n",
      "      The Witcher        570502\n",
      " Better Call Saul        524205\n",
      "   Rick and Morty        463235\n",
      "  Stranger Things        428306\n",
      "        Westworld        389641\n",
      "Dragon Ball Super        381260\n",
      "         The Boys        345460\n",
      "\n",
      "Bottom 10 shows by comment count:\n",
      "                             SHOW  NUM_COMMENTS\n",
      "                   13 Reasons Why         64740\n",
      "                        Riverdale         64665\n",
      "The Falcon and The Winter Soldier         61795\n",
      "                          Vikings         61157\n",
      "                      Moon Knight         60545\n",
      "          Orange is the New Black         52962\n",
      "                      Money Heist         47817\n",
      "              The Big Bang Theory         43888\n",
      "                          Hawkeye         22986\n",
      "                       PAW Patrol         13544\n"
     ]
    }
   ],
   "source": [
    "subreddits_df = pd.read_csv('Data/final_subreddits.csv')\n",
    "subreddits_df = subreddits_df[subreddits_df['TYPE'] != 'KDRAMA']\n",
    "\n",
    "# Step 1: Group by SHOW and sum NUM_COMMENTS\n",
    "show_comments = subreddits_df.groupby('SHOW')['NUM_COMMENTS'].sum().reset_index()\n",
    "show_comments = show_comments.sort_values('NUM_COMMENTS', ascending=False)\n",
    "\n",
    "print(\"SHOW COMMENT COUNTS\")\n",
    "print(\"=\"*60)\n",
    "print(f\"Total shows: {len(show_comments)}\")\n",
    "print(f\"Total comments: {show_comments['NUM_COMMENTS'].sum():,}\")\n",
    "print(f\"\\nComment count range: {show_comments['NUM_COMMENTS'].min():,} to {show_comments['NUM_COMMENTS'].max():,}\")\n",
    "print(f\"Mean: {show_comments['NUM_COMMENTS'].mean():,.0f}\")\n",
    "print(f\"Median: {show_comments['NUM_COMMENTS'].median():,.0f}\")\n",
    "\n",
    "# Show top 10 and bottom 10\n",
    "print(f\"\\nTop 10 shows by comment count:\")\n",
    "print(show_comments.head(10).to_string(index=False))\n",
    "\n",
    "print(f\"\\nBottom 10 shows by comment count:\")\n",
    "print(show_comments.tail(10).to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb80f928-f553-4a64-ae9c-51a17e14210d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 2: Visualize the distribution\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
    "\n",
    "# 1. Bar chart of all shows (sorted)\n",
    "axes[0,0].bar(range(len(show_comments)), show_comments['NUM_COMMENTS'])\n",
    "axes[0,0].set_xlabel('Show Rank')\n",
    "axes[0,0].set_ylabel('Number of Comments')\n",
    "axes[0,0].set_title('Comment Counts by Show (Sorted)')\n",
    "axes[0,0].set_yscale('log')  # Log scale to see full range\n",
    "\n",
    "# 2. Histogram\n",
    "axes[0,1].hist(show_comments['NUM_COMMENTS'], bins=30, edgecolor='black', alpha=0.7)\n",
    "axes[0,1].set_xlabel('Number of Comments')\n",
    "axes[0,1].set_ylabel('Number of Shows')\n",
    "axes[0,1].set_title('Distribution of Comment Counts')\n",
    "axes[0,1].axvline(show_comments['NUM_COMMENTS'].median(), color='red', \n",
    "                 linestyle='--', label=f'Median: {show_comments[\"NUM_COMMENTS\"].median():,.0f}')\n",
    "axes[0,1].legend()\n",
    "\n",
    "# 3. Log-scale histogram\n",
    "axes[0,2].hist(show_comments['NUM_COMMENTS'], bins=30, edgecolor='black', alpha=0.7)\n",
    "axes[0,2].set_xlabel('Number of Comments (log scale)')\n",
    "axes[0,2].set_ylabel('Number of Shows')\n",
    "axes[0,2].set_title('Distribution of Comment Counts (Log Scale)')\n",
    "axes[0,2].set_xscale('log')\n",
    "\n",
    "# 4. Cumulative distribution\n",
    "sorted_comments = np.sort(show_comments['NUM_COMMENTS'])\n",
    "cumulative = np.arange(1, len(sorted_comments) + 1) / len(sorted_comments)\n",
    "axes[1,0].plot(sorted_comments, cumulative, linewidth=2)\n",
    "axes[1,0].set_xlabel('Number of Comments (log scale)')\n",
    "axes[1,0].set_ylabel('Cumulative Proportion of Shows')\n",
    "axes[1,0].set_title('Cumulative Distribution')\n",
    "axes[1,0].set_xscale('log')\n",
    "axes[1,0].grid(True, alpha=0.3)\n",
    "\n",
    "# Add threshold lines for common cutoffs\n",
    "for threshold in [10000, 20000, 50000]:\n",
    "    prop = sum(show_comments['NUM_COMMENTS'] >= threshold) / len(show_comments)\n",
    "    axes[1,0].axvline(threshold, color='red', linestyle='--', alpha=0.5)\n",
    "    axes[1,0].text(threshold, 0.5, f'{threshold:,}\\n({prop:.1%} above)', \n",
    "                  rotation=90, va='center')\n",
    "\n",
    "# 5. Percentile analysis\n",
    "percentiles = [10, 25, 50, 75, 90, 95, 99]\n",
    "percentile_values = [np.percentile(show_comments['NUM_COMMENTS'], p) for p in percentiles]\n",
    "\n",
    "axes[1,1].bar(range(len(percentiles)), percentile_values, color='skyblue', edgecolor='black')\n",
    "axes[1,1].set_xticks(range(len(percentiles)))\n",
    "axes[1,1].set_xticklabels([f'{p}th' for p in percentiles])\n",
    "axes[1,1].set_ylabel('Number of Comments')\n",
    "axes[1,1].set_title('Percentile Breakpoints')\n",
    "axes[1,1].set_yscale('log')\n",
    "\n",
    "# Add value labels\n",
    "for i, val in enumerate(percentile_values):\n",
    "    axes[1,1].text(i, val, f'{val:,.0f}', ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "# 6. Box plot with potential threshold lines\n",
    "axes[1,2].boxplot(show_comments['NUM_COMMENTS'], vert=True)\n",
    "axes[1,2].set_ylabel('Number of Comments (log scale)')\n",
    "axes[1,2].set_title('Box Plot of Comment Counts')\n",
    "axes[1,2].set_yscale('log')\n",
    "\n",
    "# Add potential threshold lines\n",
    "for threshold, label in [(10000, '10K'), (20000, '20K'), (50000, '50K')]:\n",
    "    axes[1,2].axhline(threshold, color='red', linestyle='--', alpha=0.5)\n",
    "    axes[1,2].text(1.1, threshold, label, va='center')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('show_distribution_analysis.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4b169ffd-e1a1-4750-abcb-2c6602dcbfc3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "THRESHOLD ANALYSIS\n",
      "============================================================\n",
      "\n",
      "Threshold    Shows Above     % Shows      Total Comments     % Comments\n",
      "---------------------------------------------------------------------------\n",
      "     5,000           49       100.0%       12,648,198       100.0%\n",
      "    10,000           49       100.0%       12,648,198       100.0%\n",
      "    15,000           48        98.0%       12,634,654        99.9%\n",
      "    20,000           48        98.0%       12,634,654        99.9%\n",
      "    30,000           47        95.9%       12,611,668        99.7%\n",
      "    50,000           45        91.8%       12,519,963        99.0%\n",
      "\n",
      "============================================================\n",
      "SHOWS BELOW POTENTIAL THRESHOLDS\n",
      "============================================================\n",
      "\n",
      "Shows with < 15,000 comments (1 shows):\n",
      "      SHOW  NUM_COMMENTS\n",
      "PAW Patrol         13544\n",
      "\n",
      "Shows with < 20,000 comments (1 shows):\n",
      "      SHOW  NUM_COMMENTS\n",
      "PAW Patrol         13544\n",
      "\n",
      "============================================================\n",
      "STATISTICAL SUMMARY\n",
      "============================================================\n",
      "\n",
      "Quartiles:\n",
      "Q1 (25th percentile): 72,575\n",
      "Q3 (75th percentile): 289,205\n",
      "IQR: 216,630\n",
      "\n",
      "Outlier bounds (using 1.5*IQR rule):\n",
      "Lower bound: 0\n",
      "Upper bound: 614,150\n",
      "\n",
      "Low outliers: 0 shows\n",
      "\n",
      "High outliers: 3 shows\n",
      "           SHOW  NUM_COMMENTS\n",
      "Game of Thrones       2144592\n",
      "      One Piece       1622654\n",
      "Attack On Titan        994408\n"
     ]
    }
   ],
   "source": [
    "# Step 3: Analyze potential thresholds\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"THRESHOLD ANALYSIS\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "thresholds = [5000, 10000, 15000, 20000, 30000, 50000]\n",
    "\n",
    "print(f\"\\n{'Threshold':<12} {'Shows Above':<15} {'% Shows':<12} {'Total Comments':<18} {'% Comments'}\")\n",
    "print(\"-\"*75)\n",
    "\n",
    "for threshold in thresholds:\n",
    "    above_threshold = show_comments[show_comments['NUM_COMMENTS'] >= threshold]\n",
    "    n_shows = len(above_threshold)\n",
    "    pct_shows = n_shows / len(show_comments) * 100\n",
    "    total_comments = above_threshold['NUM_COMMENTS'].sum()\n",
    "    pct_comments = total_comments / show_comments['NUM_COMMENTS'].sum() * 100\n",
    "    \n",
    "    print(f\"{threshold:>10,}   {n_shows:>10}      {pct_shows:>6.1f}%     {total_comments:>12,}      {pct_comments:>6.1f}%\")\n",
    "\n",
    "# Step 4: Identify shows below common thresholds\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"SHOWS BELOW POTENTIAL THRESHOLDS\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "for threshold in [15000, 20000]:\n",
    "    below = show_comments[show_comments['NUM_COMMENTS'] < threshold]\n",
    "    print(f\"\\nShows with < {threshold:,} comments ({len(below)} shows):\")\n",
    "    if len(below) <= 20:\n",
    "        print(below.to_string(index=False))\n",
    "    else:\n",
    "        print(below.head(10).to_string(index=False))\n",
    "        print(f\"... and {len(below) - 10} more\")\n",
    "\n",
    "# Step 5: Statistical summary with outlier detection\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"STATISTICAL SUMMARY\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "Q1 = show_comments['NUM_COMMENTS'].quantile(0.25)\n",
    "Q3 = show_comments['NUM_COMMENTS'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "print(f\"\\nQuartiles:\")\n",
    "print(f\"Q1 (25th percentile): {Q1:,.0f}\")\n",
    "print(f\"Q3 (75th percentile): {Q3:,.0f}\")\n",
    "print(f\"IQR: {IQR:,.0f}\")\n",
    "\n",
    "print(f\"\\nOutlier bounds (using 1.5*IQR rule):\")\n",
    "print(f\"Lower bound: {max(0, lower_bound):,.0f}\")\n",
    "print(f\"Upper bound: {upper_bound:,.0f}\")\n",
    "\n",
    "outliers_low = show_comments[show_comments['NUM_COMMENTS'] < lower_bound]\n",
    "outliers_high = show_comments[show_comments['NUM_COMMENTS'] > upper_bound]\n",
    "\n",
    "print(f\"\\nLow outliers: {len(outliers_low)} shows\")\n",
    "if len(outliers_low) > 0:\n",
    "    print(outliers_low[['SHOW', 'NUM_COMMENTS']].to_string(index=False))\n",
    "\n",
    "print(f\"\\nHigh outliers: {len(outliers_high)} shows\")\n",
    "if len(outliers_high) > 0:\n",
    "    print(outliers_high[['SHOW', 'NUM_COMMENTS']].head(10).to_string(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1daf54a-636e-4f72-a6c1-4d23282a4477",
   "metadata": {},
   "source": [
    "### Subreddit Size Analysis (Pre/Post Squid Game)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7936d5c-43a5-49c4-86f3-e346a6e8b21e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3604140, 6)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_df = pd.read_csv('Data/Subreddit_Username_Time_Data.csv', encoding='utf-8')\n",
    "time_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "080d0e1e-d77f-4f63-9195-dde41567d24b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2456259, 7)\n",
      "(1147881, 7)\n"
     ]
    }
   ],
   "source": [
    "#split into pre and post squid game\n",
    "# Step 1: Convert the string timestamp to datetime\n",
    "time_df['DATE'] = pd.to_datetime(time_df['TIME_STAMP'], format='mixed') #format='%Y-%m-%d %H:%M')\n",
    "\n",
    "# Step 2: Define the cutoff date\n",
    "cutoff_date = pd.to_datetime('2021-09-17')\n",
    "\n",
    "# Step 3: Split the dataframe into two based on the cutoff date\n",
    "before_df = time_df[time_df['DATE'] < cutoff_date]\n",
    "after_df = time_df[time_df['DATE'] >= cutoff_date]\n",
    "\n",
    "print(before_df.shape)\n",
    "print(after_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ac6cbe22-3c7b-47ae-aa40-1211af7635a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now get counts of subreddits for before and after\n",
    "before_counts = before_df.groupby('SHOW').size()\n",
    "after_counts = after_df.groupby('SHOW').size()\n",
    "\n",
    "pre_sg = pd.DataFrame({\n",
    "    'NUM_COMMENTS': before_counts\n",
    "}).reset_index()\n",
    "\n",
    "post_sg = pd.DataFrame({\n",
    "    'NUM_COMMENTS': after_counts\n",
    "}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9728628a-87c2-4320-912f-d56c71be0cd9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SHOW COMMENT COUNTS\n",
      "============================================================\n",
      "Total shows: 49\n",
      "Total comments: 1,147,881\n",
      "\n",
      "Comment count range: 866 to 118,880\n",
      "Mean: 23,426\n",
      "Median: 9,240\n",
      "\n",
      "Top 10 shows by comment count:\n",
      "                 SHOW  NUM_COMMENTS\n",
      "            One Piece        118880\n",
      "             Euphoria        102058\n",
      "     Better Call Saul        100595\n",
      "             The Boys         86363\n",
      "          The Witcher         75292\n",
      "    The Wheel of Time         62727\n",
      "      Stranger Things         58557\n",
      "           Squid Game         53880\n",
      "The Book of Boba Fett         48312\n",
      "      Attack on Titan         38885\n",
      "\n",
      "Bottom 10 shows by comment count:\n",
      "                             SHOW  NUM_COMMENTS\n",
      "                             Loki          3780\n",
      "                       PAW Patrol          2135\n",
      "                          The 100          2087\n",
      "                             Dark          1936\n",
      "                     Black Mirror          1477\n",
      "          Orange is the New Black          1406\n",
      "                        Riverdale          1074\n",
      "The Falcon and the Winter Soldier           984\n",
      "                   13 Reasons Why           939\n",
      "                            Arrow           866\n"
     ]
    }
   ],
   "source": [
    "#RUN descriptive analysis on pre and post sg datasets\n",
    "show_comments = post_sg.sort_values('NUM_COMMENTS', ascending=False)\n",
    "\n",
    "print(\"SHOW COMMENT COUNTS\")\n",
    "print(\"=\"*60)\n",
    "print(f\"Total shows: {len(show_comments)}\")\n",
    "print(f\"Total comments: {show_comments['NUM_COMMENTS'].sum():,}\")\n",
    "print(f\"\\nComment count range: {show_comments['NUM_COMMENTS'].min():,} to {show_comments['NUM_COMMENTS'].max():,}\")\n",
    "print(f\"Mean: {show_comments['NUM_COMMENTS'].mean():,.0f}\")\n",
    "print(f\"Median: {show_comments['NUM_COMMENTS'].median():,.0f}\")\n",
    "\n",
    "# Show top 10 and bottom 10\n",
    "print(f\"\\nTop 10 shows by comment count:\")\n",
    "print(show_comments.head(10).to_string(index=False))\n",
    "\n",
    "print(f\"\\nBottom 10 shows by comment count:\")\n",
    "print(show_comments.tail(10).to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4523fd92-c6a0-4099-942d-7e852d2040f9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 2: Visualize the distribution\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
    "\n",
    "# 1. Bar chart of all shows (sorted)\n",
    "axes[0,0].bar(range(len(show_comments)), show_comments['NUM_COMMENTS'])\n",
    "axes[0,0].set_xlabel('Show Rank')\n",
    "axes[0,0].set_ylabel('Number of Comments')\n",
    "axes[0,0].set_title('Comment Counts by Show (Sorted)')\n",
    "axes[0,0].set_yscale('log')  # Log scale to see full range\n",
    "\n",
    "# 2. Histogram\n",
    "axes[0,1].hist(show_comments['NUM_COMMENTS'], bins=30, edgecolor='black', alpha=0.7)\n",
    "axes[0,1].set_xlabel('Number of Comments')\n",
    "axes[0,1].set_ylabel('Number of Shows')\n",
    "axes[0,1].set_title('Distribution of Comment Counts')\n",
    "axes[0,1].axvline(show_comments['NUM_COMMENTS'].median(), color='red', \n",
    "                 linestyle='--', label=f'Median: {show_comments[\"NUM_COMMENTS\"].median():,.0f}')\n",
    "axes[0,1].legend()\n",
    "\n",
    "# 3. Log-scale histogram\n",
    "axes[0,2].hist(show_comments['NUM_COMMENTS'], bins=30, edgecolor='black', alpha=0.7)\n",
    "axes[0,2].set_xlabel('Number of Comments (log scale)')\n",
    "axes[0,2].set_ylabel('Number of Shows')\n",
    "axes[0,2].set_title('Distribution of Comment Counts (Log Scale)')\n",
    "axes[0,2].set_xscale('log')\n",
    "\n",
    "# 4. Cumulative distribution\n",
    "sorted_comments = np.sort(show_comments['NUM_COMMENTS'])\n",
    "cumulative = np.arange(1, len(sorted_comments) + 1) / len(sorted_comments)\n",
    "axes[1,0].plot(sorted_comments, cumulative, linewidth=2)\n",
    "axes[1,0].set_xlabel('Number of Comments (log scale)')\n",
    "axes[1,0].set_ylabel('Cumulative Proportion of Shows')\n",
    "axes[1,0].set_title('Cumulative Distribution')\n",
    "axes[1,0].set_xscale('log')\n",
    "axes[1,0].grid(True, alpha=0.3)\n",
    "\n",
    "# Add threshold lines for common cutoffs\n",
    "for threshold in [10000, 20000, 50000]:\n",
    "    prop = sum(show_comments['NUM_COMMENTS'] >= threshold) / len(show_comments)\n",
    "    axes[1,0].axvline(threshold, color='red', linestyle='--', alpha=0.5)\n",
    "    axes[1,0].text(threshold, 0.5, f'{threshold:,}\\n({prop:.1%} above)', \n",
    "                  rotation=90, va='center')\n",
    "\n",
    "# 5. Percentile analysis\n",
    "percentiles = [10, 25, 50, 75, 90, 95, 99]\n",
    "percentile_values = [np.percentile(show_comments['NUM_COMMENTS'], p) for p in percentiles]\n",
    "\n",
    "axes[1,1].bar(range(len(percentiles)), percentile_values, color='skyblue', edgecolor='black')\n",
    "axes[1,1].set_xticks(range(len(percentiles)))\n",
    "axes[1,1].set_xticklabels([f'{p}th' for p in percentiles])\n",
    "axes[1,1].set_ylabel('Number of Comments')\n",
    "axes[1,1].set_title('Percentile Breakpoints')\n",
    "axes[1,1].set_yscale('log')\n",
    "\n",
    "# Add value labels\n",
    "for i, val in enumerate(percentile_values):\n",
    "    axes[1,1].text(i, val, f'{val:,.0f}', ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "# 6. Box plot with potential threshold lines\n",
    "axes[1,2].boxplot(show_comments['NUM_COMMENTS'], vert=True)\n",
    "axes[1,2].set_ylabel('Number of Comments (log scale)')\n",
    "axes[1,2].set_title('Box Plot of Comment Counts')\n",
    "axes[1,2].set_yscale('log')\n",
    "\n",
    "# Add potential threshold lines\n",
    "for threshold, label in [(10000, '10K'), (20000, '20K'), (50000, '50K')]:\n",
    "    axes[1,2].axhline(threshold, color='red', linestyle='--', alpha=0.5)\n",
    "    axes[1,2].text(1.1, threshold, label, va='center')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('show_distribution_analysis.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6b86ffb8-5139-4ad8-9b9f-b0fe29df7a77",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "THRESHOLD ANALYSIS\n",
      "============================================================\n",
      "\n",
      "Threshold    Shows Above     % Shows      Total Comments     % Comments\n",
      "---------------------------------------------------------------------------\n",
      "     5,000           35        71.4%        1,113,667        97.0%\n",
      "    10,000           21        42.9%          995,884        86.8%\n",
      "    15,000           17        34.7%          950,796        82.8%\n",
      "    20,000           17        34.7%          950,796        82.8%\n",
      "    30,000           13        26.5%          850,661        74.1%\n",
      "    50,000            8        16.3%          658,352        57.4%\n",
      "\n",
      "============================================================\n",
      "SHOWS BELOW POTENTIAL THRESHOLDS\n",
      "============================================================\n",
      "\n",
      "Shows with < 15,000 comments (32 shows):\n",
      "            SHOW  NUM_COMMENTS\n",
      "  Rick and Morty         12393\n",
      " The Mandalorian         11385\n",
      "  Peaky Blinders         10694\n",
      "         Hawkeye         10616\n",
      "    Supernatural          9894\n",
      "       Westworld          9695\n",
      "       Outlander          9242\n",
      "  Jujutsu Kaisen          9240\n",
      "My Hero Academia          9148\n",
      "The Walking Dead          8948\n",
      "... and 22 more\n",
      "\n",
      "Shows with < 20,000 comments (32 shows):\n",
      "            SHOW  NUM_COMMENTS\n",
      "  Rick and Morty         12393\n",
      " The Mandalorian         11385\n",
      "  Peaky Blinders         10694\n",
      "         Hawkeye         10616\n",
      "    Supernatural          9894\n",
      "       Westworld          9695\n",
      "       Outlander          9242\n",
      "  Jujutsu Kaisen          9240\n",
      "My Hero Academia          9148\n",
      "The Walking Dead          8948\n",
      "... and 22 more\n",
      "\n",
      "============================================================\n",
      "STATISTICAL SUMMARY\n",
      "============================================================\n",
      "\n",
      "Quartiles:\n",
      "Q1 (25th percentile): 4,403\n",
      "Q3 (75th percentile): 30,876\n",
      "IQR: 26,473\n",
      "\n",
      "Outlier bounds (using 1.5*IQR rule):\n",
      "Lower bound: 0\n",
      "Upper bound: 70,586\n",
      "\n",
      "Low outliers: 0 shows\n",
      "\n",
      "High outliers: 5 shows\n",
      "            SHOW  NUM_COMMENTS\n",
      "       One Piece        118880\n",
      "        Euphoria        102058\n",
      "Better Call Saul        100595\n",
      "        The Boys         86363\n",
      "     The Witcher         75292\n"
     ]
    }
   ],
   "source": [
    "# Step 3: Analyze potential thresholds\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"THRESHOLD ANALYSIS\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "thresholds = [5000, 10000, 15000, 20000, 30000, 50000]\n",
    "\n",
    "print(f\"\\n{'Threshold':<12} {'Shows Above':<15} {'% Shows':<12} {'Total Comments':<18} {'% Comments'}\")\n",
    "print(\"-\"*75)\n",
    "\n",
    "for threshold in thresholds:\n",
    "    above_threshold = show_comments[show_comments['NUM_COMMENTS'] >= threshold]\n",
    "    n_shows = len(above_threshold)\n",
    "    pct_shows = n_shows / len(show_comments) * 100\n",
    "    total_comments = above_threshold['NUM_COMMENTS'].sum()\n",
    "    pct_comments = total_comments / show_comments['NUM_COMMENTS'].sum() * 100\n",
    "    \n",
    "    print(f\"{threshold:>10,}   {n_shows:>10}      {pct_shows:>6.1f}%     {total_comments:>12,}      {pct_comments:>6.1f}%\")\n",
    "\n",
    "# Step 4: Identify shows below common thresholds\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"SHOWS BELOW POTENTIAL THRESHOLDS\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "for threshold in [15000, 20000]:\n",
    "    below = show_comments[show_comments['NUM_COMMENTS'] < threshold]\n",
    "    print(f\"\\nShows with < {threshold:,} comments ({len(below)} shows):\")\n",
    "    if len(below) <= 20:\n",
    "        print(below.to_string(index=False))\n",
    "    else:\n",
    "        print(below.head(10).to_string(index=False))\n",
    "        print(f\"... and {len(below) - 10} more\")\n",
    "\n",
    "# Step 5: Statistical summary with outlier detection\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"STATISTICAL SUMMARY\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "Q1 = show_comments['NUM_COMMENTS'].quantile(0.25)\n",
    "Q3 = show_comments['NUM_COMMENTS'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "print(f\"\\nQuartiles:\")\n",
    "print(f\"Q1 (25th percentile): {Q1:,.0f}\")\n",
    "print(f\"Q3 (75th percentile): {Q3:,.0f}\")\n",
    "print(f\"IQR: {IQR:,.0f}\")\n",
    "\n",
    "print(f\"\\nOutlier bounds (using 1.5*IQR rule):\")\n",
    "print(f\"Lower bound: {max(0, lower_bound):,.0f}\")\n",
    "print(f\"Upper bound: {upper_bound:,.0f}\")\n",
    "\n",
    "outliers_low = show_comments[show_comments['NUM_COMMENTS'] < lower_bound]\n",
    "outliers_high = show_comments[show_comments['NUM_COMMENTS'] > upper_bound]\n",
    "\n",
    "print(f\"\\nLow outliers: {len(outliers_low)} shows\")\n",
    "if len(outliers_low) > 0:\n",
    "    print(outliers_low[['SHOW', 'NUM_COMMENTS']].to_string(index=False))\n",
    "\n",
    "print(f\"\\nHigh outliers: {len(outliers_high)} shows\")\n",
    "if len(outliers_high) > 0:\n",
    "    print(outliers_high[['SHOW', 'NUM_COMMENTS']].head(10).to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1f74f5dd-c17f-4d2b-a513-3b9aba723d36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bridgerton', 'Euphoria', 'Hawkeye', 'Moon Knight', 'PAW Patrol', 'The Big Bang Theory', 'The Book of Boba Fett', 'The Wheel of Time'] ['13 Reasons Why', 'American Horror Story', 'Arrow', 'Black Mirror', 'Brooklyn Nine-Nine', 'Dark', \"Grey's Anatomy\", 'Jujutsu Kaisen', 'La Casa De Papel', 'Loki', 'Lucifer', \"Marvel's Agents of SHIELD\", 'My Hero Academia', 'Orange is the New Black', 'Outlander', 'PAW Patrol', 'Riverdale', 'South Park', 'SpongeBob SquarePants', 'Supernatural', 'The 100', 'The Big Bang Theory', 'The Falcon and the Winter Soldier', 'The Flash', 'The Walking Dead', 'Vikings', 'WandaVision', 'Westworld']\n"
     ]
    }
   ],
   "source": [
    "threshold = 10000\n",
    "pre_exclude = pre_sg[pre_sg['NUM_COMMENTS'] < threshold]['SHOW'].tolist()\n",
    "post_exclude = post_sg[post_sg['NUM_COMMENTS'] < threshold]['SHOW'].tolist()\n",
    "\n",
    "print(pre_exclude, post_exclude)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11bd9bf8-43b1-4da6-9795-0b3d04292351",
   "metadata": {},
   "source": [
    "### Add KDrama Data for User Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "82afae34-20b0-4fae-99aa-e546e6b43cbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9663, 7)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import user comment data by subreddit\n",
    "kdf = pd.read_csv('Data/KDrama_User_Comment_Counts_By_Subreddit.csv', encoding='utf-8')\n",
    "kdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "03d58f1a-129d-4033-95dd-acc5991db889",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SUBREDDIT</th>\n",
       "      <th>SHOW</th>\n",
       "      <th>SUBREDDIT_TOTAL</th>\n",
       "      <th>USERNAME</th>\n",
       "      <th>RAW_COUNT</th>\n",
       "      <th>PERCENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170</td>\n",
       "      <td>11.929825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>Qui-Gon_Winn</td>\n",
       "      <td>1</td>\n",
       "      <td>0.070175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>zero0n3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.421053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.070175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>Maximum-Product-1255</td>\n",
       "      <td>29</td>\n",
       "      <td>2.035088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0           SUBREDDIT       SHOW  SUBREDDIT_TOTAL  \\\n",
       "0           0  HellboundonNetflix  Hellbound             1425   \n",
       "1           1  HellboundonNetflix  Hellbound             1425   \n",
       "2           2  HellboundonNetflix  Hellbound             1425   \n",
       "3           3  HellboundonNetflix  Hellbound             1425   \n",
       "4           4  HellboundonNetflix  Hellbound             1425   \n",
       "\n",
       "               USERNAME  RAW_COUNT    PERCENT  \n",
       "0                   NaN        170  11.929825  \n",
       "1          Qui-Gon_Winn          1   0.070175  \n",
       "2               zero0n3          6   0.421053  \n",
       "3                   NaN          1   0.070175  \n",
       "4  Maximum-Product-1255         29   2.035088  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8d40d272-73a6-4b9e-baba-7842be135621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SHOW\n",
       "All of Us are Dead           13832\n",
       "Crash Landing on You         12273\n",
       "Hellbound                     1425\n",
       "Hometown Cha Cha Cha          1523\n",
       "Kingdom                        241\n",
       "Mister Sunshine                 48\n",
       "Office Blind Date              140\n",
       "Our Beloved Summer             341\n",
       "Silent Sea                     549\n",
       "Snowdrop                         7\n",
       "Sweet Home                    8702\n",
       "The King: Eternal Monarch       66\n",
       "True Beauty                   1338\n",
       "Twenty-Five Twenty-One         795\n",
       "Vincenzo                       552\n",
       "Name: RAW_COUNT, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kdf.groupby('SHOW')['RAW_COUNT'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "58d547b9-ad7a-4e0a-819f-4b0f782b2d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41832"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kdf['RAW_COUNT'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9d9911f-d998-41d6-9fca-b4de52490eb6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            count      mean        std  min  25%  50%  75%  \\\n",
      "SHOW                                                                         \n",
      "All of Us are Dead         2939.0  4.706363  29.490128  1.0  1.0  2.0  4.0   \n",
      "Crash Landing on You       1508.0  8.138594  33.790408  1.0  1.0  2.0  5.0   \n",
      "Hellbound                   517.0  2.756286   8.408340  1.0  1.0  1.0  2.0   \n",
      "Hometown Cha Cha Cha        405.0  3.760494   8.759686  1.0  1.0  1.0  3.0   \n",
      "Kingdom                     132.0  1.825758   2.671581  1.0  1.0  1.0  2.0   \n",
      "Mister Sunshine              11.0  4.363636   5.954372  1.0  1.0  1.0  4.0   \n",
      "Office Blind Date            84.0  1.666667   1.903284  1.0  1.0  1.0  2.0   \n",
      "Our Beloved Summer          129.0  2.643411   3.106944  1.0  1.0  1.0  3.0   \n",
      "Silent Sea                  246.0  2.231707   4.047571  1.0  1.0  1.0  2.0   \n",
      "Snowdrop                      7.0  1.000000   0.000000  1.0  1.0  1.0  1.0   \n",
      "Sweet Home                 2831.0  3.073826  16.233843  1.0  1.0  1.0  2.0   \n",
      "The King: Eternal Monarch    36.0  1.833333   1.664761  1.0  1.0  1.0  2.0   \n",
      "True Beauty                 341.0  3.923754  10.997863  1.0  1.0  1.0  3.0   \n",
      "Twenty-Five Twenty-One      270.0  2.944444   4.837957  1.0  1.0  1.0  3.0   \n",
      "Vincenzo                    207.0  2.666667   4.220845  1.0  1.0  1.0  3.0   \n",
      "\n",
      "                              max  \n",
      "SHOW                               \n",
      "All of Us are Dead         1502.0  \n",
      "Crash Landing on You        846.0  \n",
      "Hellbound                   170.0  \n",
      "Hometown Cha Cha Cha        113.0  \n",
      "Kingdom                      29.0  \n",
      "Mister Sunshine              18.0  \n",
      "Office Blind Date            16.0  \n",
      "Our Beloved Summer           21.0  \n",
      "Silent Sea                   49.0  \n",
      "Snowdrop                      1.0  \n",
      "Sweet Home                  779.0  \n",
      "The King: Eternal Monarch     9.0  \n",
      "True Beauty                 175.0  \n",
      "Twenty-Five Twenty-One       44.0  \n",
      "Vincenzo                     45.0  \n"
     ]
    }
   ],
   "source": [
    "#get the distribution of raw_count by subreddit\n",
    "grouped_stats = kdf.groupby('SHOW')['RAW_COUNT'].describe()\n",
    "\n",
    "print(grouped_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c9854ab1-e24f-45d8-b2ed-fd5505769224",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "644.2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(grouped_stats['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef93f939-609e-4662-b9f1-3edab45639c8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hoytlong\\AppData\\Local\\Temp\\ipykernel_15088\\3688073811.py:6: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  top_n_per_group = kdf.groupby('SHOW').apply(get_top_n)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SUBREDDIT</th>\n",
       "      <th>SHOW</th>\n",
       "      <th>SUBREDDIT_TOTAL</th>\n",
       "      <th>USERNAME</th>\n",
       "      <th>RAW_COUNT</th>\n",
       "      <th>PERCENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025</td>\n",
       "      <td>AllOfUsAreDead</td>\n",
       "      <td>All of Us are Dead</td>\n",
       "      <td>13832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1502</td>\n",
       "      <td>10.858878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2126</td>\n",
       "      <td>AllOfUsAreDead</td>\n",
       "      <td>All of Us are Dead</td>\n",
       "      <td>13832</td>\n",
       "      <td>Sudden_Pop_2279</td>\n",
       "      <td>235</td>\n",
       "      <td>1.698959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2027</td>\n",
       "      <td>AllOfUsAreDead</td>\n",
       "      <td>All of Us are Dead</td>\n",
       "      <td>13832</td>\n",
       "      <td>JJDude</td>\n",
       "      <td>156</td>\n",
       "      <td>1.127820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2217</td>\n",
       "      <td>AllOfUsAreDead</td>\n",
       "      <td>All of Us are Dead</td>\n",
       "      <td>13832</td>\n",
       "      <td>cayc615</td>\n",
       "      <td>125</td>\n",
       "      <td>0.903702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2062</td>\n",
       "      <td>AllOfUsAreDead</td>\n",
       "      <td>All of Us are Dead</td>\n",
       "      <td>13832</td>\n",
       "      <td>yazzy1233</td>\n",
       "      <td>118</td>\n",
       "      <td>0.853094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>523</td>\n",
       "      <td>CrashLandingOnYou</td>\n",
       "      <td>Crash Landing on You</td>\n",
       "      <td>12273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>846</td>\n",
       "      <td>6.893180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>553</td>\n",
       "      <td>CrashLandingOnYou</td>\n",
       "      <td>Crash Landing on You</td>\n",
       "      <td>12273</td>\n",
       "      <td>FantasticWin8988</td>\n",
       "      <td>612</td>\n",
       "      <td>4.986556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>548</td>\n",
       "      <td>CrashLandingOnYou</td>\n",
       "      <td>Crash Landing on You</td>\n",
       "      <td>12273</td>\n",
       "      <td>fuzzybella</td>\n",
       "      <td>340</td>\n",
       "      <td>2.770309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>541</td>\n",
       "      <td>CrashLandingOnYou</td>\n",
       "      <td>Crash Landing on You</td>\n",
       "      <td>12273</td>\n",
       "      <td>SkyeHoon1927</td>\n",
       "      <td>271</td>\n",
       "      <td>2.208099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>596</td>\n",
       "      <td>CrashLandingOnYou</td>\n",
       "      <td>Crash Landing on You</td>\n",
       "      <td>12273</td>\n",
       "      <td>DansoRoboto</td>\n",
       "      <td>245</td>\n",
       "      <td>1.996252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170</td>\n",
       "      <td>11.929825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>29</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>DominoBarksdale</td>\n",
       "      <td>62</td>\n",
       "      <td>4.350877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>103</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>duh_hana</td>\n",
       "      <td>32</td>\n",
       "      <td>2.245614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>Maximum-Product-1255</td>\n",
       "      <td>29</td>\n",
       "      <td>2.035088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>249</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>thenokvok</td>\n",
       "      <td>24</td>\n",
       "      <td>1.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8492</td>\n",
       "      <td>hometownchachacha</td>\n",
       "      <td>Hometown Cha Cha Cha</td>\n",
       "      <td>1523</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113</td>\n",
       "      <td>7.419567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8490</td>\n",
       "      <td>hometownchachacha</td>\n",
       "      <td>Hometown Cha Cha Cha</td>\n",
       "      <td>1523</td>\n",
       "      <td>CaptCryptoMoon</td>\n",
       "      <td>89</td>\n",
       "      <td>5.843729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8517</td>\n",
       "      <td>hometownchachacha</td>\n",
       "      <td>Hometown Cha Cha Cha</td>\n",
       "      <td>1523</td>\n",
       "      <td>fuzzybella</td>\n",
       "      <td>47</td>\n",
       "      <td>3.086014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8511</td>\n",
       "      <td>hometownchachacha</td>\n",
       "      <td>Hometown Cha Cha Cha</td>\n",
       "      <td>1523</td>\n",
       "      <td>gusu_melody</td>\n",
       "      <td>37</td>\n",
       "      <td>2.429416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8553</td>\n",
       "      <td>hometownchachacha</td>\n",
       "      <td>Hometown Cha Cha Cha</td>\n",
       "      <td>1523</td>\n",
       "      <td>rtsen</td>\n",
       "      <td>34</td>\n",
       "      <td>2.232436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9528</td>\n",
       "      <td>KingdomNetflix</td>\n",
       "      <td>Kingdom</td>\n",
       "      <td>241</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>12.033195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9531</td>\n",
       "      <td>KingdomNetflix</td>\n",
       "      <td>Kingdom</td>\n",
       "      <td>241</td>\n",
       "      <td>panickedpris</td>\n",
       "      <td>7</td>\n",
       "      <td>2.904564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9522</td>\n",
       "      <td>KingdomNetflix</td>\n",
       "      <td>Kingdom</td>\n",
       "      <td>241</td>\n",
       "      <td>luvprue1</td>\n",
       "      <td>6</td>\n",
       "      <td>2.489627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9547</td>\n",
       "      <td>KingdomNetflix</td>\n",
       "      <td>Kingdom</td>\n",
       "      <td>241</td>\n",
       "      <td>Lynda73</td>\n",
       "      <td>6</td>\n",
       "      <td>2.489627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>9582</td>\n",
       "      <td>KingdomNetflix</td>\n",
       "      <td>Kingdom</td>\n",
       "      <td>241</td>\n",
       "      <td>yoohoooos</td>\n",
       "      <td>6</td>\n",
       "      <td>2.489627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9653</td>\n",
       "      <td>MisterSunshine</td>\n",
       "      <td>Mister Sunshine</td>\n",
       "      <td>48</td>\n",
       "      <td>vanished_cabinet</td>\n",
       "      <td>18</td>\n",
       "      <td>37.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9657</td>\n",
       "      <td>MisterSunshine</td>\n",
       "      <td>Mister Sunshine</td>\n",
       "      <td>48</td>\n",
       "      <td>charmaine54321</td>\n",
       "      <td>14</td>\n",
       "      <td>29.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9652</td>\n",
       "      <td>MisterSunshine</td>\n",
       "      <td>Mister Sunshine</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9654</td>\n",
       "      <td>MisterSunshine</td>\n",
       "      <td>Mister Sunshine</td>\n",
       "      <td>48</td>\n",
       "      <td>IamlovelyRita</td>\n",
       "      <td>3</td>\n",
       "      <td>6.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>9655</td>\n",
       "      <td>MisterSunshine</td>\n",
       "      <td>Mister Sunshine</td>\n",
       "      <td>48</td>\n",
       "      <td>setlib</td>\n",
       "      <td>2</td>\n",
       "      <td>4.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>8423</td>\n",
       "      <td>businessproposal</td>\n",
       "      <td>Office Blind Date</td>\n",
       "      <td>140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>11.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>8430</td>\n",
       "      <td>businessproposal</td>\n",
       "      <td>Office Blind Date</td>\n",
       "      <td>140</td>\n",
       "      <td>viwi-</td>\n",
       "      <td>7</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>8420</td>\n",
       "      <td>businessproposal</td>\n",
       "      <td>Office Blind Date</td>\n",
       "      <td>140</td>\n",
       "      <td>flamebed</td>\n",
       "      <td>6</td>\n",
       "      <td>4.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>8480</td>\n",
       "      <td>businessproposal</td>\n",
       "      <td>Office Blind Date</td>\n",
       "      <td>140</td>\n",
       "      <td>Emperorluffyvj</td>\n",
       "      <td>5</td>\n",
       "      <td>3.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>8412</td>\n",
       "      <td>businessproposal</td>\n",
       "      <td>Office Blind Date</td>\n",
       "      <td>140</td>\n",
       "      <td>ohmyarchons</td>\n",
       "      <td>3</td>\n",
       "      <td>2.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>8912</td>\n",
       "      <td>OurBelovedSummer</td>\n",
       "      <td>Our Beloved Summer</td>\n",
       "      <td>341</td>\n",
       "      <td>Sunnyandginger</td>\n",
       "      <td>21</td>\n",
       "      <td>6.158358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>8923</td>\n",
       "      <td>OurBelovedSummer</td>\n",
       "      <td>Our Beloved Summer</td>\n",
       "      <td>341</td>\n",
       "      <td>Jaded-Signature-3182</td>\n",
       "      <td>14</td>\n",
       "      <td>4.105572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>8961</td>\n",
       "      <td>OurBelovedSummer</td>\n",
       "      <td>Our Beloved Summer</td>\n",
       "      <td>341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>4.105572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>8931</td>\n",
       "      <td>OurBelovedSummer</td>\n",
       "      <td>Our Beloved Summer</td>\n",
       "      <td>341</td>\n",
       "      <td>whytheHnot30</td>\n",
       "      <td>12</td>\n",
       "      <td>3.519062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>8909</td>\n",
       "      <td>OurBelovedSummer</td>\n",
       "      <td>Our Beloved Summer</td>\n",
       "      <td>341</td>\n",
       "      <td>HotLatte_299</td>\n",
       "      <td>10</td>\n",
       "      <td>2.932551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>9242</td>\n",
       "      <td>SilentSea</td>\n",
       "      <td>Silent Sea</td>\n",
       "      <td>549</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>8.925319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>9264</td>\n",
       "      <td>SilentSea</td>\n",
       "      <td>Silent Sea</td>\n",
       "      <td>549</td>\n",
       "      <td>CoconutDust</td>\n",
       "      <td>27</td>\n",
       "      <td>4.918033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>9327</td>\n",
       "      <td>SilentSea</td>\n",
       "      <td>Silent Sea</td>\n",
       "      <td>549</td>\n",
       "      <td>Ok_Bite8099</td>\n",
       "      <td>19</td>\n",
       "      <td>3.460838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>9341</td>\n",
       "      <td>SilentSea</td>\n",
       "      <td>Silent Sea</td>\n",
       "      <td>549</td>\n",
       "      <td>Upbeat-Drop6918</td>\n",
       "      <td>15</td>\n",
       "      <td>2.732240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>9325</td>\n",
       "      <td>SilentSea</td>\n",
       "      <td>Silent Sea</td>\n",
       "      <td>549</td>\n",
       "      <td>HewchyFPS</td>\n",
       "      <td>14</td>\n",
       "      <td>2.550091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>9024</td>\n",
       "      <td>seolganghwa</td>\n",
       "      <td>Snowdrop</td>\n",
       "      <td>7</td>\n",
       "      <td>geministan-97</td>\n",
       "      <td>1</td>\n",
       "      <td>14.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>9025</td>\n",
       "      <td>seolganghwa</td>\n",
       "      <td>Snowdrop</td>\n",
       "      <td>7</td>\n",
       "      <td>Hikanah</td>\n",
       "      <td>1</td>\n",
       "      <td>14.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>9026</td>\n",
       "      <td>seolganghwa</td>\n",
       "      <td>Snowdrop</td>\n",
       "      <td>7</td>\n",
       "      <td>AvailableSense4984</td>\n",
       "      <td>1</td>\n",
       "      <td>14.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>9027</td>\n",
       "      <td>seolganghwa</td>\n",
       "      <td>Snowdrop</td>\n",
       "      <td>7</td>\n",
       "      <td>Psychological_Law879</td>\n",
       "      <td>1</td>\n",
       "      <td>14.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>9028</td>\n",
       "      <td>seolganghwa</td>\n",
       "      <td>Snowdrop</td>\n",
       "      <td>7</td>\n",
       "      <td>J-Midori</td>\n",
       "      <td>1</td>\n",
       "      <td>14.285714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0           SUBREDDIT                  SHOW  SUBREDDIT_TOTAL  \\\n",
       "0         2025      AllOfUsAreDead    All of Us are Dead            13832   \n",
       "1         2126      AllOfUsAreDead    All of Us are Dead            13832   \n",
       "2         2027      AllOfUsAreDead    All of Us are Dead            13832   \n",
       "3         2217      AllOfUsAreDead    All of Us are Dead            13832   \n",
       "4         2062      AllOfUsAreDead    All of Us are Dead            13832   \n",
       "5          523   CrashLandingOnYou  Crash Landing on You            12273   \n",
       "6          553   CrashLandingOnYou  Crash Landing on You            12273   \n",
       "7          548   CrashLandingOnYou  Crash Landing on You            12273   \n",
       "8          541   CrashLandingOnYou  Crash Landing on You            12273   \n",
       "9          596   CrashLandingOnYou  Crash Landing on You            12273   \n",
       "10           0  HellboundonNetflix             Hellbound             1425   \n",
       "11          29  HellboundonNetflix             Hellbound             1425   \n",
       "12         103  HellboundonNetflix             Hellbound             1425   \n",
       "13           4  HellboundonNetflix             Hellbound             1425   \n",
       "14         249  HellboundonNetflix             Hellbound             1425   \n",
       "15        8492   hometownchachacha  Hometown Cha Cha Cha             1523   \n",
       "16        8490   hometownchachacha  Hometown Cha Cha Cha             1523   \n",
       "17        8517   hometownchachacha  Hometown Cha Cha Cha             1523   \n",
       "18        8511   hometownchachacha  Hometown Cha Cha Cha             1523   \n",
       "19        8553   hometownchachacha  Hometown Cha Cha Cha             1523   \n",
       "20        9528      KingdomNetflix               Kingdom              241   \n",
       "21        9531      KingdomNetflix               Kingdom              241   \n",
       "22        9522      KingdomNetflix               Kingdom              241   \n",
       "23        9547      KingdomNetflix               Kingdom              241   \n",
       "24        9582      KingdomNetflix               Kingdom              241   \n",
       "25        9653      MisterSunshine       Mister Sunshine               48   \n",
       "26        9657      MisterSunshine       Mister Sunshine               48   \n",
       "27        9652      MisterSunshine       Mister Sunshine               48   \n",
       "28        9654      MisterSunshine       Mister Sunshine               48   \n",
       "29        9655      MisterSunshine       Mister Sunshine               48   \n",
       "30        8423    businessproposal     Office Blind Date              140   \n",
       "31        8430    businessproposal     Office Blind Date              140   \n",
       "32        8420    businessproposal     Office Blind Date              140   \n",
       "33        8480    businessproposal     Office Blind Date              140   \n",
       "34        8412    businessproposal     Office Blind Date              140   \n",
       "35        8912    OurBelovedSummer    Our Beloved Summer              341   \n",
       "36        8923    OurBelovedSummer    Our Beloved Summer              341   \n",
       "37        8961    OurBelovedSummer    Our Beloved Summer              341   \n",
       "38        8931    OurBelovedSummer    Our Beloved Summer              341   \n",
       "39        8909    OurBelovedSummer    Our Beloved Summer              341   \n",
       "40        9242           SilentSea            Silent Sea              549   \n",
       "41        9264           SilentSea            Silent Sea              549   \n",
       "42        9327           SilentSea            Silent Sea              549   \n",
       "43        9341           SilentSea            Silent Sea              549   \n",
       "44        9325           SilentSea            Silent Sea              549   \n",
       "45        9024         seolganghwa              Snowdrop                7   \n",
       "46        9025         seolganghwa              Snowdrop                7   \n",
       "47        9026         seolganghwa              Snowdrop                7   \n",
       "48        9027         seolganghwa              Snowdrop                7   \n",
       "49        9028         seolganghwa              Snowdrop                7   \n",
       "\n",
       "                USERNAME  RAW_COUNT    PERCENT  \n",
       "0                    NaN       1502  10.858878  \n",
       "1        Sudden_Pop_2279        235   1.698959  \n",
       "2                 JJDude        156   1.127820  \n",
       "3                cayc615        125   0.903702  \n",
       "4              yazzy1233        118   0.853094  \n",
       "5                    NaN        846   6.893180  \n",
       "6       FantasticWin8988        612   4.986556  \n",
       "7             fuzzybella        340   2.770309  \n",
       "8           SkyeHoon1927        271   2.208099  \n",
       "9            DansoRoboto        245   1.996252  \n",
       "10                   NaN        170  11.929825  \n",
       "11       DominoBarksdale         62   4.350877  \n",
       "12              duh_hana         32   2.245614  \n",
       "13  Maximum-Product-1255         29   2.035088  \n",
       "14             thenokvok         24   1.684211  \n",
       "15                   NaN        113   7.419567  \n",
       "16        CaptCryptoMoon         89   5.843729  \n",
       "17            fuzzybella         47   3.086014  \n",
       "18           gusu_melody         37   2.429416  \n",
       "19                 rtsen         34   2.232436  \n",
       "20                   NaN         29  12.033195  \n",
       "21          panickedpris          7   2.904564  \n",
       "22              luvprue1          6   2.489627  \n",
       "23               Lynda73          6   2.489627  \n",
       "24             yoohoooos          6   2.489627  \n",
       "25      vanished_cabinet         18  37.500000  \n",
       "26        charmaine54321         14  29.166667  \n",
       "27                   NaN          5  10.416667  \n",
       "28         IamlovelyRita          3   6.250000  \n",
       "29                setlib          2   4.166667  \n",
       "30                   NaN         16  11.428571  \n",
       "31                 viwi-          7   5.000000  \n",
       "32              flamebed          6   4.285714  \n",
       "33        Emperorluffyvj          5   3.571429  \n",
       "34           ohmyarchons          3   2.142857  \n",
       "35        Sunnyandginger         21   6.158358  \n",
       "36  Jaded-Signature-3182         14   4.105572  \n",
       "37                   NaN         14   4.105572  \n",
       "38          whytheHnot30         12   3.519062  \n",
       "39          HotLatte_299         10   2.932551  \n",
       "40                   NaN         49   8.925319  \n",
       "41           CoconutDust         27   4.918033  \n",
       "42           Ok_Bite8099         19   3.460838  \n",
       "43       Upbeat-Drop6918         15   2.732240  \n",
       "44             HewchyFPS         14   2.550091  \n",
       "45         geministan-97          1  14.285714  \n",
       "46               Hikanah          1  14.285714  \n",
       "47    AvailableSense4984          1  14.285714  \n",
       "48  Psychological_Law879          1  14.285714  \n",
       "49              J-Midori          1  14.285714  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to get the top N commenters for each subreddit\n",
    "def get_top_n(group, n=5):\n",
    "    return group.nlargest(n, 'RAW_COUNT')\n",
    "\n",
    "# Group by 'Column_1', apply the function, and include 'Column_2' in the result\n",
    "top_n_per_group = kdf.groupby('SHOW').apply(get_top_n)\n",
    "\n",
    "# Reset index if necessary to clean up the DataFrame\n",
    "top_n_per_group.reset_index(drop=True, inplace=True)\n",
    "\n",
    "top_n_per_group[0:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a1b47b72-d518-4157-b85f-013021ff807a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hoytlong\\AppData\\Local\\Temp\\ipykernel_15144\\1401164115.py:9: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  results = kdf.groupby('SHOW').apply(calculate_counts_and_percentage)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count     15.000000\n",
       "mean      60.156439\n",
       "std       13.455245\n",
       "min       44.098143\n",
       "25%       52.270531\n",
       "50%       55.813953\n",
       "75%       66.666667\n",
       "max      100.000000\n",
       "Name: Perc_One_Counts, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a function to apply to each group\n",
    "def calculate_counts_and_percentage(group):\n",
    "    total_count = group.shape[0]  # Total number of rows in the group\n",
    "    count_of_one = (group['RAW_COUNT'] == 1).sum()  # Count of 'RAW_COUNT' == 1\n",
    "    percentage_of_one = (count_of_one / total_count) * 100  # Calculate the percentage\n",
    "    return pd.Series({'Count_of_One': count_of_one, 'Perc_One_Counts': percentage_of_one})\n",
    "\n",
    "# Group by 'Column_1' and apply the function\n",
    "results = kdf.groupby('SHOW').apply(calculate_counts_and_percentage)\n",
    "\n",
    "results[\"Perc_One_Counts\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dee46c1a-406b-4f64-9ad1-551a8eaac772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4533, 7)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rows where 'USER' is 'None' or 'AutoModerator'\n",
    "condition1 = ~kdf['USERNAME'].isin(['None', 'AutoModerator', 'NaN'])\n",
    "# Rows where 'RAW_COUNT' is 1\n",
    "condition2 = kdf['RAW_COUNT'] != 1\n",
    "\n",
    "# Combine conditions using the logical AND operator\n",
    "combined_conditions = condition1 & condition2\n",
    "\n",
    "# Filter the DataFrame\n",
    "filtered_kdf = kdf[combined_conditions]\n",
    "\n",
    "# also remove nan values\n",
    "filtered_kdf = filtered_kdf.dropna(subset=['USERNAME'])\n",
    "\n",
    "filtered_kdf.shape  #this gives us total number of users who leave more than 1 comment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8fcaee12-b481-4525-90cd-c9ad4b9e9c53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SUBREDDIT</th>\n",
       "      <th>SHOW</th>\n",
       "      <th>SUBREDDIT_TOTAL</th>\n",
       "      <th>USERNAME</th>\n",
       "      <th>RAW_COUNT</th>\n",
       "      <th>PERCENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>zero0n3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.421053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>Maximum-Product-1255</td>\n",
       "      <td>29</td>\n",
       "      <td>2.035088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>Doctora_House</td>\n",
       "      <td>3</td>\n",
       "      <td>0.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>dimensionalApe</td>\n",
       "      <td>2</td>\n",
       "      <td>0.140351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>HellboundonNetflix</td>\n",
       "      <td>Hellbound</td>\n",
       "      <td>1425</td>\n",
       "      <td>_Democracy_</td>\n",
       "      <td>6</td>\n",
       "      <td>0.421053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0           SUBREDDIT       SHOW  SUBREDDIT_TOTAL  \\\n",
       "2           2  HellboundonNetflix  Hellbound             1425   \n",
       "4           4  HellboundonNetflix  Hellbound             1425   \n",
       "5           5  HellboundonNetflix  Hellbound             1425   \n",
       "6           6  HellboundonNetflix  Hellbound             1425   \n",
       "7           7  HellboundonNetflix  Hellbound             1425   \n",
       "\n",
       "               USERNAME  RAW_COUNT   PERCENT  \n",
       "2               zero0n3          6  0.421053  \n",
       "4  Maximum-Product-1255         29  2.035088  \n",
       "5         Doctora_House          3  0.210526  \n",
       "6        dimensionalApe          2  0.140351  \n",
       "7           _Democracy_          6  0.421053  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_kdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "62a7da25-332a-4c58-92eb-762075beb46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SUBREDDIT</th>\n",
       "      <th>SHOW</th>\n",
       "      <th>SUBREDDIT_TOTAL</th>\n",
       "      <th>USERNAME</th>\n",
       "      <th>RAW_COUNT</th>\n",
       "      <th>PERCENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>'r/mandalorian'</td>\n",
       "      <td>Mandalorian</td>\n",
       "      <td>32923</td>\n",
       "      <td>Josiah_404</td>\n",
       "      <td>7</td>\n",
       "      <td>0.021262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>'r/mandalorian'</td>\n",
       "      <td>Mandalorian</td>\n",
       "      <td>32923</td>\n",
       "      <td>Goliath_Gamer</td>\n",
       "      <td>2</td>\n",
       "      <td>0.006075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>'r/mandalorian'</td>\n",
       "      <td>Mandalorian</td>\n",
       "      <td>32923</td>\n",
       "      <td>GOpencyprep</td>\n",
       "      <td>3</td>\n",
       "      <td>0.009112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>'r/mandalorian'</td>\n",
       "      <td>Mandalorian</td>\n",
       "      <td>32923</td>\n",
       "      <td>F1r3spray</td>\n",
       "      <td>5</td>\n",
       "      <td>0.015187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>'r/mandalorian'</td>\n",
       "      <td>Mandalorian</td>\n",
       "      <td>32923</td>\n",
       "      <td>gibby1476</td>\n",
       "      <td>2</td>\n",
       "      <td>0.006075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        SUBREDDIT         SHOW  SUBREDDIT_TOTAL       USERNAME  \\\n",
       "4           4  'r/mandalorian'  Mandalorian            32923     Josiah_404   \n",
       "5           5  'r/mandalorian'  Mandalorian            32923  Goliath_Gamer   \n",
       "6           6  'r/mandalorian'  Mandalorian            32923    GOpencyprep   \n",
       "8           8  'r/mandalorian'  Mandalorian            32923      F1r3spray   \n",
       "9           9  'r/mandalorian'  Mandalorian            32923      gibby1476   \n",
       "\n",
       "   RAW_COUNT   PERCENT  \n",
       "4          7  0.021262  \n",
       "5          2  0.006075  \n",
       "6          3  0.009112  \n",
       "8          5  0.015187  \n",
       "9          2  0.006075  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#need to strip start and end quotation marks in the global hits dataset\n",
    "filtered_df['USERNAME'] = filtered_df['USERNAME'].str.strip(\"'\")\n",
    "filtered_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f09e3977-3101-4386-ae7d-e1b6c56c85c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "998"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see how many usernames in KDrama dataset overlap with Global Hits dataset\n",
    "# this is after excluding 1 comment users\n",
    "joint_size = len(set(filtered_df['USERNAME'].to_list()) & set(filtered_kdf['USERNAME'].to_list()))\n",
    "joint_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "69cdb064-f87f-4d9c-a314-ad6b82a039c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(703300, 7)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine dataframes and reset index\n",
    "combined_df = pd.concat([filtered_df, filtered_kdf], ignore_index=True)\n",
    "combined_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "56b8b539-1b80-4b04-a55e-eab64174901d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SUBREDDIT</th>\n",
       "      <th>SHOW</th>\n",
       "      <th>SUBREDDIT_TOTAL</th>\n",
       "      <th>USERNAME</th>\n",
       "      <th>RAW_COUNT</th>\n",
       "      <th>PERCENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>328644</th>\n",
       "      <td>767702</td>\n",
       "      <td>'r/bridgertonnetflix'</td>\n",
       "      <td>Bridgerton</td>\n",
       "      <td>68558</td>\n",
       "      <td>Padme501st</td>\n",
       "      <td>14</td>\n",
       "      <td>0.020421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328645</th>\n",
       "      <td>767705</td>\n",
       "      <td>'r/bridgertonnetflix'</td>\n",
       "      <td>Bridgerton</td>\n",
       "      <td>68558</td>\n",
       "      <td>lavinient</td>\n",
       "      <td>7</td>\n",
       "      <td>0.010210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328646</th>\n",
       "      <td>767706</td>\n",
       "      <td>'r/bridgertonnetflix'</td>\n",
       "      <td>Bridgerton</td>\n",
       "      <td>68558</td>\n",
       "      <td>meatball77</td>\n",
       "      <td>129</td>\n",
       "      <td>0.188162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328647</th>\n",
       "      <td>767708</td>\n",
       "      <td>'r/bridgertonnetflix'</td>\n",
       "      <td>Bridgerton</td>\n",
       "      <td>68558</td>\n",
       "      <td>Fife-</td>\n",
       "      <td>305</td>\n",
       "      <td>0.444879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328648</th>\n",
       "      <td>767709</td>\n",
       "      <td>'r/bridgertonnetflix'</td>\n",
       "      <td>Bridgerton</td>\n",
       "      <td>68558</td>\n",
       "      <td>pigeonlover13</td>\n",
       "      <td>3</td>\n",
       "      <td>0.004376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0              SUBREDDIT        SHOW  SUBREDDIT_TOTAL  \\\n",
       "328644      767702  'r/bridgertonnetflix'  Bridgerton            68558   \n",
       "328645      767705  'r/bridgertonnetflix'  Bridgerton            68558   \n",
       "328646      767706  'r/bridgertonnetflix'  Bridgerton            68558   \n",
       "328647      767708  'r/bridgertonnetflix'  Bridgerton            68558   \n",
       "328648      767709  'r/bridgertonnetflix'  Bridgerton            68558   \n",
       "\n",
       "             USERNAME  RAW_COUNT   PERCENT  \n",
       "328644     Padme501st         14  0.020421  \n",
       "328645      lavinient          7  0.010210  \n",
       "328646     meatball77        129  0.188162  \n",
       "328647          Fife-        305  0.444879  \n",
       "328648  pigeonlover13          3  0.004376  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df[combined_df['SHOW']==\"Bridgerton\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f9d6df70-2260-4359-bf55-0cc668e0f90c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(319835, 7)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now produce a table that, for every username, lists all the subreddits to which they contribute along with raw count\n",
    "#and percent for that subreddit\n",
    "\n",
    "#basically, this just means subsetting to all usernames who appear in more than one subreddit\n",
    "\n",
    "# Count unique groups for each USERNAME\n",
    "subreddit_counts = combined_df.groupby('USERNAME')['SUBREDDIT'].nunique()\n",
    "\n",
    "# Filter for usernames appearing in 2 or more subreddits\n",
    "multiple_subreddits = subreddit_counts[subreddit_counts >= 2].index\n",
    "\n",
    "# Filter the DataFrame based on the usernames identified\n",
    "new_df = combined_df[combined_df['USERNAME'].isin(multiple_subreddits)]\n",
    "\n",
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cb0c16bf-0dca-43f8-ab66-cb24cc62afa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "115175"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of unique users who comment on multiple subreddits in this dataset\n",
    "len(multiple_subreddits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "58f57999-2cc2-497e-ba8b-d3b0a04fc4d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SHOW</th>\n",
       "      <th>USERNAME</th>\n",
       "      <th>RAW_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>-Archillion</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>-BINK2014-</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>-Dean_Winchester-</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>-Jamberry-</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>-MassiveDynamic-</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SHOW           USERNAME  RAW_COUNT\n",
       "0  13 Reasons Why        -Archillion          3\n",
       "1  13 Reasons Why         -BINK2014-          7\n",
       "2  13 Reasons Why  -Dean_Winchester-          2\n",
       "3  13 Reasons Why         -Jamberry-          9\n",
       "4  13 Reasons Why   -MassiveDynamic-         10"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now group the users by SHOW, rather than SUBREDDIT, so that we have total comments by show\n",
    "user_by_show = new_df.groupby(['SHOW', 'USERNAME']).agg({'RAW_COUNT': 'sum'}).reset_index()\n",
    "user_by_show.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5e786da6-ff2c-4ac3-bf04-0044de36fe3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#need to do this fix because there is a user with the name Bridgerton\n",
    "user_by_show.loc[user_by_show['USERNAME'] == 'Bridgerton', 'USERNAME'] = \"'Bridgerton'\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a774083-9dca-4475-8336-712831faa0c7",
   "metadata": {},
   "source": [
    "### Size Analysis with Combined Global Super Hit and KDrama datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4b6abb41-5688-43a0-88e3-565512732d2c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Membership Size Statistics:\n",
      "count       63.000000\n",
      "mean      4553.333333\n",
      "std       5814.224748\n",
      "min          2.000000\n",
      "25%       1125.000000\n",
      "50%       2929.000000\n",
      "75%       5668.000000\n",
      "max      36208.000000\n",
      "Name: NUM_MEMBERS, dtype: float64\n",
      "\n",
      "Smallest subreddits:\n",
      "                         SHOW  NUM_MEMBERS\n",
      "35    Orange is the New Black         1437\n",
      "10                 Bridgerton         1431\n",
      "21                    Hawkeye         1393\n",
      "44                  SpongeBob         1254\n",
      "7             Big Bang Theory          996\n",
      "2          All of Us are Dead          497\n",
      "48                 Sweet Home          299\n",
      "13       Crash Landing on You          147\n",
      "22                  Hellbound           83\n",
      "38                 PAW Patrol           81\n",
      "23       Hometown Cha Cha Cha           36\n",
      "42                 Silent Sea           30\n",
      "55                True Beauty           24\n",
      "59                   Vincenzo           22\n",
      "25                    Kingdom           18\n",
      "56     Twenty-Five Twenty-One           16\n",
      "33          Office Blind Date           10\n",
      "36         Our Beloved Summer            9\n",
      "52  The King: Eternal Monarch            3\n",
      "29            Mister Sunshine            2\n",
      "\n",
      "Largest subreddits:\n",
      "                  SHOW  NUM_MEMBERS\n",
      "18     Game of Thrones        36208\n",
      "54         The Witcher        16060\n",
      "5      Attack On Titan        15026\n",
      "50            The Boys        14259\n",
      "40      Rick and Morty        13517\n",
      "28         Mandalorian        12866\n",
      "46     Stranger Things        12164\n",
      "11  Brooklyn Nine-Nine         9909\n",
      "34           One Piece         9454\n",
      "6     Better Call Saul         8583\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Subreddits below thresholds:\n",
      "  Below 10 members: 3 subreddits\n",
      "  Below 25 members: 8 subreddits\n",
      "  Below 50 members: 10 subreddits\n",
      "  Below 100 members: 12 subreddits\n",
      "  Below 200 members: 13 subreddits\n"
     ]
    }
   ],
   "source": [
    "# Get the number of unique users per show/subreddit\n",
    "membership_size = user_by_show.groupby('SHOW')['USERNAME'].nunique().reset_index()\n",
    "membership_size.columns = ['SHOW', 'NUM_MEMBERS']\n",
    "\n",
    "# Sort by size\n",
    "membership_size = membership_size.sort_values('NUM_MEMBERS', ascending=False)\n",
    "\n",
    "# Display basic statistics\n",
    "print(\"Membership Size Statistics:\")\n",
    "print(membership_size['NUM_MEMBERS'].describe())\n",
    "print(\"\\nSmallest subreddits:\")\n",
    "print(membership_size.tail(20))\n",
    "print(\"\\nLargest subreddits:\")\n",
    "print(membership_size.head(10))\n",
    "\n",
    "# Visualize the distribution\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Histogram\n",
    "axes[0].hist(membership_size['NUM_MEMBERS'], bins=30, edgecolor='black')\n",
    "axes[0].set_xlabel('Number of Members')\n",
    "axes[0].set_ylabel('Number of Subreddits')\n",
    "axes[0].set_title('Distribution of Subreddit Membership Size')\n",
    "\n",
    "# Log scale histogram (useful if there's a wide range)\n",
    "axes[1].hist(membership_size['NUM_MEMBERS'], bins=30, edgecolor='black')\n",
    "axes[1].set_xlabel('Number of Members')\n",
    "axes[1].set_ylabel('Number of Subreddits')\n",
    "axes[1].set_title('Distribution (Log Scale)')\n",
    "axes[1].set_xscale('log')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Check how many subreddits fall below certain thresholds\n",
    "thresholds = [10, 25, 50, 100, 200]\n",
    "print(\"\\nSubreddits below thresholds:\")\n",
    "for threshold in thresholds:\n",
    "    count = (membership_size['NUM_MEMBERS'] < threshold).sum()\n",
    "    print(f\"  Below {threshold} members: {count} subreddits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0cd58785-7596-4330-a26b-c353f8888c53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create aggregated categories to deal with small membership size of Kdrama subreddits\n",
    "user_by_show_aggregated = user_by_show.copy()\n",
    "\n",
    "korean_horror_shows = ['Kingdom', 'Sweet Home', 'Hellbound', 'All of Us are Dead']  # your horror shows\n",
    "korean_romance_shows = ['Hometown Cha Cha Cha', 'True Beauty', 'Crash Landing on You', 'Silent Sea', 'True Beauty', 'Vincenzo', 'Twenty-Five Twenty-One',\n",
    "                       'Office Blind Date', 'Our Beloved Summer', 'The King: Eternal Monarch', 'Mister Sunshine']  # your romance shows\n",
    "\n",
    "user_by_show_aggregated.loc[user_by_show_aggregated['SHOW'].isin(korean_horror_shows), 'SHOW'] = 'Korean Zombie or Horror'\n",
    "user_by_show_aggregated.loc[user_by_show_aggregated['SHOW'].isin(korean_romance_shows), 'SHOW'] = 'Korean Romance or Historical'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fd51cba-ea74-4165-99ff-54eaee37a6d2",
   "metadata": {},
   "source": [
    "### Cluster subreddits by user overlap (Figure 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7ec8b197-d691-458a-9486-85b9fbb72566",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nodes: 51\n",
      "Edges: 1274\n"
     ]
    }
   ],
   "source": [
    "#jaccard similarity approach\n",
    "\n",
    "from networkx.algorithms import bipartite\n",
    "\n",
    "# Create a bipartite graph\n",
    "B = nx.Graph()\n",
    "\n",
    "# Add nodes with the bipartite attribute\n",
    "shows = user_by_show_aggregated['SHOW'].unique()\n",
    "users = user_by_show_aggregated['USERNAME'].unique()\n",
    "B.add_nodes_from(shows, bipartite=0)  # Show nodes\n",
    "B.add_nodes_from(users, bipartite=1)  # User nodes\n",
    "\n",
    "# Add edges with weights\n",
    "for _, row in user_by_show_aggregated.iterrows():\n",
    "    B.add_edge(row['SHOW'], row['USERNAME'], weight=row['RAW_COUNT'])\n",
    "    \n",
    "# Project onto the show nodes\n",
    "# This will create a unipartite graph where shows are nodes and edges represent shared user interactions\n",
    "projected_graph = bipartite.overlap_weighted_projected_graph(B, shows, jaccard=True)\n",
    "\n",
    "# Print the resulting edges and their weights\n",
    "#for edge in projected_graph.edges(data=True):\n",
    "#    print(edge)\n",
    "\n",
    "print(\"Nodes:\", len(projected_graph.nodes()))\n",
    "print(\"Edges:\", len(projected_graph.edges()))\n",
    "G = projected_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0abc94aa-91f2-4cfb-b6dd-e06ca60e4cda",
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.write_edgelist(G, \"test.csv\", delimiter=',', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5f7622ab-5f3f-4c0c-8ea4-e6fbde963f49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get adjacency matrix\n",
    "adj_matrix = nx.to_numpy_array(G)\n",
    "\n",
    "# Create distance matrix (ensuring diagonal is 0)\n",
    "distances = 1 - adj_matrix\n",
    "np.fill_diagonal(distances, 0)  # Set diagonal to 0\n",
    "\n",
    "# Perform hierarchical clustering\n",
    "linkage = hierarchy.linkage(squareform(distances), method='ward')\n",
    "\n",
    "# Plot\n",
    "# Create figure with specific size\n",
    "plt.figure(figsize=(6, 8))  \n",
    "\n",
    "# Set a color threshold to determine when to split into different colors\n",
    "# This can be a specific distance value from your linkage; I'm setting the value based on number of desired clusters\n",
    "n_clusters = 10  #this is an arbitrary value set to faciliate visibility of groupings\n",
    "color_threshold_value = linkage[-(n_clusters-1), 2]\n",
    "\n",
    "#color_threshold_value = 0.25 * max(linkage[:, 2])  # 70% of max distance, adjust as needed\n",
    "\n",
    "# Create dendrogram subplot\n",
    "dendro_plot = hierarchy.dendrogram(linkage, \n",
    "                                  labels=shows,\n",
    "                                  orientation='right',\n",
    "                                  leaf_rotation=0,\n",
    "                                  color_threshold=color_threshold_value,\n",
    "                                  above_threshold_color='gray')  # Color for links above threshold)\n",
    "plt.xlabel('Distance')\n",
    "plt.tick_params(axis='y', labelsize=7)\n",
    "plt.axvline(x=color_threshold_value, c='red', linestyle='--', linewidth=1)  # Optional: show threshold\n",
    "\n",
    "# Highlight \"Squid Game\" in red\n",
    "ax = plt.gca()\n",
    "labels = ax.get_yticklabels()\n",
    "for label in labels:\n",
    "    if label.get_text() == \"Squid Game\":\n",
    "        label.set_color('red')\n",
    "        label.set_weight('bold')  # Optional: also make it bold\n",
    "\n",
    "# Save as high-resolution JPEG\n",
    "plt.savefig('Images/UserClustersFullData.jpg', dpi=400, bbox_inches='tight', format='jpeg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ed4673a-132e-4478-b6ac-27248180882b",
   "metadata": {},
   "source": [
    "### Analysis of user overlap across different subreddits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "513a1617-72df-47d5-8408-f3ccb5942cab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Squid Game members: 4446\n",
      "KDrama members: 1067\n",
      "Also in KDrama subreddits: 228 (5.1%)\n",
      "NOT in KDrama subreddits: 4218 (94.9%)\n"
     ]
    }
   ],
   "source": [
    "squid_members = set(user_by_show[user_by_show['SHOW'] == 'Squid Game']['USERNAME'])\n",
    "korean_horror_members = set(user_by_show_aggregated[user_by_show_aggregated['SHOW'] == 'Korean Zombie or Horror']['USERNAME'])\n",
    "korean_romance_members = set(user_by_show_aggregated[user_by_show_aggregated['SHOW'] == 'Korean Romance or Historical']['USERNAME'])\n",
    "\n",
    "# Combine all KDrama community members\n",
    "kdrama_members = korean_horror_members | korean_romance_members\n",
    "\n",
    "# Calculate overlaps\n",
    "squid_also_kdrama = len(squid_members & kdrama_members)\n",
    "squid_only = len(squid_members - kdrama_members)\n",
    "\n",
    "print(f\"Squid Game members: {len(squid_members)}\")\n",
    "print(f\"KDrama members: {len(kdrama_members)}\")\n",
    "print(f\"Also in KDrama subreddits: {squid_also_kdrama} ({100*squid_also_kdrama/len(squid_members):.1f}%)\")\n",
    "print(f\"NOT in KDrama subreddits: {squid_only} ({100*squid_only/len(squid_members):.1f}%)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "16ecfd4f-3bd6-43e2-9bf4-0dc9bb24ecca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SHOW</th>\n",
       "      <th>USERNAME</th>\n",
       "      <th>RAW_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>-Archillion</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>-BINK2014-</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>-Dean_Winchester-</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>-Jamberry-</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13 Reasons Why</td>\n",
       "      <td>-MassiveDynamic-</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             SHOW           USERNAME  RAW_COUNT\n",
       "0  13 Reasons Why        -Archillion          3\n",
       "1  13 Reasons Why         -BINK2014-          7\n",
       "2  13 Reasons Why  -Dean_Winchester-          2\n",
       "3  13 Reasons Why         -Jamberry-          9\n",
       "4  13 Reasons Why   -MassiveDynamic-         10"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_by_show_aggregated.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d38cb190-5ebf-4141-b944-d6c4eba3ef85",
   "metadata": {},
   "source": [
    "### Analyze diversity and concentration of users in subreddits (provides data for Figure 13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d3c7f6b6-e5f4-4898-a72e-7a76ec12b054",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            Show  Avg_Subreddits_Per_User  \\\n",
      "21                       Hawkeye                 4.452979   \n",
      "17     Falcon and Winter Soldier                 4.304210   \n",
      "4                          Arrow                 4.161438   \n",
      "27                   Moon Knight                 4.103030   \n",
      "23                          Loki                 4.004424   \n",
      "49                   WandaVision                 3.974239   \n",
      "32                    PAW Patrol                 3.950617   \n",
      "1               Agents of SHIELD                 3.943484   \n",
      "43                     The Flash                 3.929184   \n",
      "9              Book of Boba Fett                 3.767841   \n",
      "35                     Riverdale                 3.718812   \n",
      "24                       Lucifer                 3.709466   \n",
      "42                      The Boys                 3.607827   \n",
      "47                       Vikings                 3.603581   \n",
      "46              Umbrella Academy                 3.592177   \n",
      "25                   Mandalorian                 3.579590   \n",
      "48                  Walking Dead                 3.566410   \n",
      "30       Orange is the New Black                 3.565066   \n",
      "12                     Cobra Kai                 3.557867   \n",
      "8                   Black Mirror                 3.554109   \n",
      "50                     Westworld                 3.550518   \n",
      "39               Stranger Things                 3.542585   \n",
      "0                 13 Reasons Why                 3.531789   \n",
      "7                Big Bang Theory                 3.517068   \n",
      "41                       The 100                 3.479520   \n",
      "11            Brooklyn Nine-Nine                 3.470179   \n",
      "38                    Squid Game                 3.454341   \n",
      "3          American Horror Story                 3.448264   \n",
      "33                Peaky Blinders                 3.427842   \n",
      "2        Korean Zombie or Horror                 3.418033   \n",
      "40                  Supernatural                 3.390718   \n",
      "14                          Dark                 3.374766   \n",
      "36                    South Park                 3.345126   \n",
      "26                   Money Heist                 3.340627   \n",
      "44             The Wheel of Time                 3.283478   \n",
      "37                     SpongeBob                 3.279107   \n",
      "34                Rick and Morty                 3.274691   \n",
      "10                    Bridgerton                 3.266946   \n",
      "6               Better Call Saul                 3.266923   \n",
      "19                 Greys Anatomy                 3.217300   \n",
      "16                      Euphoria                 3.185648   \n",
      "31                     Outlander                 3.145530   \n",
      "28              My Hero Academia                 3.134797   \n",
      "13  Korean Romance or Historical                 3.130612   \n",
      "22                Jujutsu Kaisen                 3.102424   \n",
      "45                   The Witcher                 3.001245   \n",
      "15             Dragon Ball Super                 2.922906   \n",
      "18               Game of Thrones                 2.814019   \n",
      "20               Handmaid's Tale                 2.804316   \n",
      "29                     One Piece                 2.745081   \n",
      "5                Attack On Titan                 2.421603   \n",
      "\n",
      "    Median_Subreddits_Per_User  Std_Subreddits_Per_User  \n",
      "21                         3.0                 3.278409  \n",
      "17                         3.0                 3.042931  \n",
      "4                          3.0                 3.231619  \n",
      "27                         3.0                 3.187344  \n",
      "23                         3.0                 2.894633  \n",
      "49                         3.0                 2.714114  \n",
      "32                         2.0                 5.398845  \n",
      "1                          3.0                 2.953740  \n",
      "43                         3.0                 2.924491  \n",
      "9                          3.0                 2.751903  \n",
      "35                         3.0                 3.202609  \n",
      "24                         3.0                 3.172974  \n",
      "42                         3.0                 2.380854  \n",
      "47                         3.0                 3.076621  \n",
      "46                         3.0                 2.862517  \n",
      "25                         3.0                 2.439840  \n",
      "48                         3.0                 2.516118  \n",
      "30                         3.0                 2.650113  \n",
      "12                         3.0                 2.579454  \n",
      "8                          3.0                 2.480622  \n",
      "50                         3.0                 2.322572  \n",
      "39                         3.0                 2.298939  \n",
      "0                          3.0                 2.773963  \n",
      "7                          2.0                 3.628573  \n",
      "41                         3.0                 2.510346  \n",
      "11                         3.0                 2.401857  \n",
      "38                         3.0                 2.707662  \n",
      "3                          3.0                 2.806107  \n",
      "33                         3.0                 2.987411  \n",
      "2                          2.0                 3.203486  \n",
      "40                         2.0                 2.948422  \n",
      "14                         2.0                 2.700032  \n",
      "36                         3.0                 2.387514  \n",
      "26                         2.0                 3.141797  \n",
      "44                         2.0                 2.940672  \n",
      "37                         2.0                 3.587056  \n",
      "34                         3.0                 2.210562  \n",
      "10                         2.0                 3.087537  \n",
      "6                          3.0                 2.202927  \n",
      "19                         2.0                 3.087337  \n",
      "16                         2.0                 2.414763  \n",
      "31                         2.0                 2.319704  \n",
      "28                         2.0                 2.268337  \n",
      "13                         2.0                 4.196906  \n",
      "22                         2.0                 2.759687  \n",
      "45                         2.0                 2.200232  \n",
      "15                         2.0                 2.258146  \n",
      "18                         2.0                 1.839363  \n",
      "20                         2.0                 2.304616  \n",
      "29                         2.0                 2.068223  \n",
      "5                          2.0                 2.003228  \n"
     ]
    }
   ],
   "source": [
    "# For each show, calculate how many other subreddits their members participate in\n",
    "participation_diversity = []\n",
    "\n",
    "for show in user_by_show_aggregated['SHOW'].unique():\n",
    "    # Get users who post to this show\n",
    "    show_users = user_by_show_aggregated[user_by_show_aggregated['SHOW'] == show]['USERNAME'].unique()\n",
    "    \n",
    "    # For each user, count how many total shows they participate in\n",
    "    user_show_counts = user_by_show_aggregated[user_by_show_aggregated['USERNAME'].isin(show_users)].groupby('USERNAME')['SHOW'].nunique()\n",
    "    \n",
    "    participation_diversity.append({\n",
    "        'Show': show,\n",
    "        'Avg_Subreddits_Per_User': user_show_counts.mean(),\n",
    "        'Median_Subreddits_Per_User': user_show_counts.median(),\n",
    "        'Std_Subreddits_Per_User': user_show_counts.std()\n",
    "    })\n",
    "\n",
    "diversity_df = pd.DataFrame(participation_diversity).sort_values('Avg_Subreddits_Per_User', ascending=False)\n",
    "print(diversity_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ca5d1b99-f4ba-43b9-b589-3fcfb9ee244d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            Show  Pct_In_Top5  Pct_In_Top10\n",
      "35                     Riverdale    23.947114     42.287504\n",
      "46              Umbrella Academy    28.915437     46.299740\n",
      "24                       Lucifer    27.317868     46.326450\n",
      "41                       The 100    28.773936     46.493371\n",
      "26                   Money Heist    27.881457     46.999516\n",
      "40                  Supernatural    27.335261     47.119012\n",
      "20               Handmaid's Tale    31.195966     48.153106\n",
      "12                     Cobra Kai    28.909265     48.560309\n",
      "16                      Euphoria    32.321657     48.894226\n",
      "0                 13 Reasons Why    30.608894     49.441719\n",
      "38                    Squid Game    31.675574     49.733534\n",
      "48                  Walking Dead    32.769881     50.315734\n",
      "39               Stranger Things    33.149864     50.406551\n",
      "7                Big Bang Theory    29.862049     50.797334\n",
      "3          American Horror Story    35.781255     51.104568\n",
      "37                     SpongeBob    30.731609     51.199110\n",
      "2        Korean Zombie or Horror    33.136582     51.370396\n",
      "14                          Dark    35.066032     52.619313\n",
      "42                      The Boys    37.068977     53.932804\n",
      "19                 Greys Anatomy    36.781352     54.288916\n",
      "47                       Vikings    38.937707     54.321422\n",
      "33                Peaky Blinders    37.527834     54.342099\n",
      "43                     The Flash    36.536512     54.888629\n",
      "8                   Black Mirror    35.379983     54.946848\n",
      "10                    Bridgerton    31.723337     55.225533\n",
      "1               Agents of SHIELD    35.915354     55.377733\n",
      "18               Game of Thrones    35.014956     55.689554\n",
      "6               Better Call Saul    37.989660     55.751059\n",
      "30       Orange is the New Black    37.514992     56.378028\n",
      "11            Brooklyn Nine-Nine    39.500002     56.825175\n",
      "13  Korean Romance or Historical    36.900068     57.104011\n",
      "44             The Wheel of Time    39.136374     58.271684\n",
      "49                   WandaVision    40.344359     58.570926\n",
      "4                          Arrow    42.265122     59.482847\n",
      "31                     Outlander    41.256455     59.876356\n",
      "45                   The Witcher    44.878434     60.231650\n",
      "50                     Westworld    42.065931     60.464453\n",
      "34                Rick and Morty    41.906735     60.897551\n",
      "25                   Mandalorian    42.538832     61.070474\n",
      "27                   Moon Knight    39.591014     61.871781\n",
      "23                          Loki    45.092821     61.989870\n",
      "36                    South Park    44.348639     63.798469\n",
      "17     Falcon and Winter Soldier    45.607111     63.845604\n",
      "9              Book of Boba Fett    45.515824     64.835747\n",
      "5                Attack On Titan    46.774093     67.119316\n",
      "21                       Hawkeye    46.304954     67.232323\n",
      "15             Dragon Ball Super    48.908532     67.374361\n",
      "29                     One Piece    55.689758     72.638789\n",
      "28              My Hero Academia    56.887839     73.422907\n",
      "32                    PAW Patrol    47.748779     73.993222\n",
      "22                Jujutsu Kaisen    63.935160     78.724551\n"
     ]
    }
   ],
   "source": [
    "concentration = []\n",
    "\n",
    "for show in user_by_show_aggregated['SHOW'].unique():\n",
    "    show_users = user_by_show_aggregated[user_by_show_aggregated['SHOW'] == show]['USERNAME'].unique()\n",
    "    user_posts = user_by_show_aggregated[user_by_show_aggregated['USERNAME'].isin(show_users)]\n",
    "    \n",
    "    # Distribution of posts across other subreddits\n",
    "    other_subreddits = user_posts[user_posts['SHOW'] != show].groupby('SHOW')['RAW_COUNT'].sum().sort_values(ascending=False)\n",
    "    \n",
    "    if len(other_subreddits) > 0:\n",
    "        total_posts = other_subreddits.sum()\n",
    "        top5_posts = other_subreddits.head(5).sum()\n",
    "        top10_posts = other_subreddits.head(10).sum()\n",
    "        \n",
    "        concentration.append({\n",
    "            'Show': show,\n",
    "            'Pct_In_Top5': (top5_posts / total_posts * 100) if total_posts > 0 else 0,\n",
    "            'Pct_In_Top10': (top10_posts / total_posts * 100) if total_posts > 0 else 0\n",
    "        })\n",
    "\n",
    "concentration_df = pd.DataFrame(concentration).sort_values('Pct_In_Top10', ascending=True)\n",
    "print(concentration_df)"
   ]
  }
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